The Mandarin Chinese Speech Database: A Large Corpus for Auditory Neutral Nonsense Pseudo-Sentences

The Mandarin Chinese Speech Database: A Large Corpus for Auditory Neutral Nonsense Pseudo-Sentences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report The Mandarin Chinese Speech Database: A Large Corpus for Auditory Neutral Nonsense Pseudo-Sentences Anqi Zhou, Qiuhong Li, Chao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4702345/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Language Resources and Evaluation → Version 1 posted 9 You are reading this latest preprint version Abstract Word frequency, context, and length are three core elements that impact speech perception. Considering the limitations of previous Chinese stimulus databases, such as non-standardized sentence structures, uncontrolled emotional information that may exist in semantics, and a relatively small number of voice items, we developed an abundant and reliable Chinese Mandarin nonsense pseudo-sentences database with fixed syntax (pronoun + subject + adverbial + predicate + pronoun + object), lengths (6 two-character words), and high-frequency words in daily life. The high-frequency keywords (subject, predicate, and object) were extracted from China Daily. Ten native Chinese participants (five women and five men) evaluated the sentences. After removing sentences with potential emotional and semantic content valence, 3,148 meaningless neutral sentence text remained. The sentences were recorded by six native speakers (three males and three females) with broadcasting experience in a neutral tone. After examining and standardizing all the voices, 18,820 audio files were included in the corpus ( https://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f ). For each speaker, 12 acoustic parameters (duration, F0 mean, F0 standard deviation, F0 minimum, F0 maximum, harmonics-to-noise ratio, jitter, shimmer, in-tensity, root-mean-square amplitude, spectral center of gravity, and spectral spread) were retrieved, and there were significant gender differences in the acoustic features (all p < 0.001). This database could be valuable for researchers and clinicians to investigate rich topics, such as children’s reading ability, speech recognition abilities in different populations, and oral cues for orofacial movement training in stutterers. Speech Perception Chinese meaningless sentences Voices Acoustic features Neutral stimuli Introduction Correctly perceiving speech contexts in communication is crucial in everyday life. However, it could be difficult for listeners to understand accurate information in noisy environments, like the Cocktail Party Effect (McDermott, 2009 ; Cherry, 1953 ). Interest in speech recognition has grown in recent years both in auditory research and clinical practice (Jones & Freyman, 2012 ; Alho et al., 2021 ; Wang et al., 2019 ). Materials assessing the ability to recognize speech include single words (Peterson & Lehiste, 1962 ; Wilson, 1993 ; Cheoy et al., 2021 ), spondees (Zhang et al., 2006 ; Conn et al., 1975 ), and sentences (Bolia et al., 2000 ; Nielsen et al., 2014 ; O'Neill et al., 2020 ; Hagerman, 1982 ; Nuesse et al., 2019 ). Compared to isolated words and spondees, sentences better represent the auditory scenarios encountered in real-life listening environments, and receiving sentence information requires a more comprehensive set of linguistic computation, thus providing insights into naturalistic speech processing (Alho et al., 2021 ; Rossell et al., 1998 ). Moreover, in sentence recognition tasks, participants could respond to multiple keywords per trial, which enhances efficiency compared to single-word recognition tasks (Jett et al., 2021 ). Frequency, context, and length of speech stimuli are three dominating factors for word recognition accuracy (Grosjean, 1980 ). Discriminating words with low frequency might confuse participants and lead to unreliable outcomes (Peterson & Lehiste, 1962 ). Using frequently used words could reduce the inconsistency in familiarity with the words among different listener groups (Calandruccio & Smiljanic, 2012 ). Sentence length could increase linguistic complexity (van der Hoek-Snieders & Rhebergen, 2023 ). Therefore, several studies have developed closed-set sentences to control the length. For example, a study utilized Coordinate Response Measure (a call sign and a color-number combination) to construct sentences (Bolia et al., 2000 ), while some use sentences structured as “name + verb + number + adjective + noun” as stimuli (Hagerman, 1982 ; Kroll et al., 2018 ). Nevertheless, the vocabulary used in these structured sentences is limited, restricting listeners’ response options and reducing the effectiveness of tests (Nielsen et al., 2014 ). The factor of context has also aroused increasing interest in recent years. Based on the presence or absence of syntactic and semantic context, sentence corpora developed in previous studies have two categories: meaningful and meaningless sentences. The former are sentences with correct grammatical structure and semantic information, such as AzBio sentence lists (Spahr et al., 2012 ) and the Bamford-Kowal-Bench British sentence test (Bench et al., 1979 ), grounded in daily conversations and ideas. Another type is pseudo-sentence with syntactic structure but without lexical meaning (Tao et al., 2017 ). Compared with meaningless sentences, keyword recognition improves when using semantic sentences to demonstrate words (Zhang et al., 2018 ; Van Engen et al., 2014 ; Kalikow et al., 1977 ). Meaningful sentences are highly predictable since context would give cues and limit the vocabulary pool of possible keywords, requiring less capacity of listeners to understand audible acoustic-phonetic information for speech recognition (Wasiuk et al., 2022 ). In the daily listening environment with multi-talkers, pseudo-sentences are commonly used speech stimuli because of their abstract nature and low predictability. Pseudo-sentences make listeners focus on the acoustic properties of the speech rather than the sentence meanings (Steiner et al., 2022 ). Additionally, the unpredictability of words in pseudo-sentences reduces the effect of long-term memory or prior knowledge of listeners (das Graças de Souza et al., 2013 ). In addition to serving as stimuli for speech perception, meaningless sentences are materials used to train or measure participants’ abilities in emotional recognition, reading, orofacial movements, and short-term memory (Service et al., 2022 ; Zupan & Eskritt, 2022 ; McClean et al., 2004 ; Yan et al., 2018 ). To our knowledge, there were few Mandarin Chinese databases containing pseudo-sentences. Three studies used pseudo-sentences to assess the emotion discrimination ability of participants (Gong et al., 2023 ; Liu & Pell, 2012 ; Paulmann & Uskul, 2014 ). Two databases contained sentences created by researchers without providing detailed information (Liu & Pell, 2012 ; Paulmann & Uskul, 2014 ). Two studies did not control the emotional (positive, negative, or neutral) valence expressed in neutral sentences (Paulmann & Uskul, 2014 ; Yang et al., 2007 ). The number of meaningless sentences included in the above databases is also limited. In this study, we developed and validated a large auditory nonsense sentence corpus, built on the commonly used subject-verb-object (SVO) order in Mandarin Chinese (Su & Naigles, 2019 ) and matched to the existing sentence database in terms of the number of keywords, and sentence length (Gong et al., 2023 ; Yang et al., 2007 ). All the keywords were high-frequency disyllable in news reports, and each appeared in a sentence only once. Besides, we conducted a pilot study to ensure all sentences were meaningless and neutral. Then, we invited six native speakers (three males and three females) with broadcasting experience to record the sentences in a neutral tone. Finally, we performed energy balance and standardization on these sentences. Methods Sentence Development Keywords (subject, verb, and object) in the nonsense sentences were disyllabic words, based on the articles from China Daily by February 25, 2024. THULAC (in Python) package (Sun et al., 2016) was used to segment words and mark their parts of speech. The noun and verb list were ranked according to the frequency of word occurrence, containing 49,249 nouns and 33,871 verbs without duplicate words. Two researchers checked the word lists, removing reduplicative words, words containing English letters, or words with verb-object structure and obtaining 45,634 nouns and 22,171 verbs. A total of 12,140 nouns and 6,070 verbs with a high frequency of occurrence were randomly combined into 6070 syntactically but not semantically correct sentences with the frame of subject (noun) + predicate (verb) + object (noun). Next, a double-syllable pronoun was inserted before each noun, and an auxiliary word was inserted before the verb in a sentence to achieve a phonetic balance (Yang et al., 2007; Feng et al., 2018). Accordingly, the entire sentence consisted of 12 Chinese characters (pronoun + subject + adverbial + predicate + pronoun + object), and the word-by-word English translation of it was similar but not identical to the English meaningless sentences constructed by Helfer and Rossell (Rossell et al., 1998; Helfer, 1997). For example, one translated pseudo-sentence “他的甲烷可能传播两个皮带” was “His methane can broadcast two belts ,” where the keywords are underlined. Among the 6070 sentences, 1000 were removed because they contained rare characters, semantics, or emotions. Finally, 5070 sentences remained for the preprocessing step before recording. Preprocessing of Sentence Text Participants Ten native Mandarin speakers (five females and five males; mean age = 24.7 ± 3.7 years) were recruited through online advertisement. Each participant completed sociodemographic information, the Chinese version of the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the nine-item Patient Health Questionnaire Depression Scale (PHQ-9). Their PHQ-9 and GAD-6 scores were all less than 10 (Costantini et al., 2021; Toussaint et al., 2020), and their years of education were all above 16. Each participant was compensated ￥100 for their engagement. Evaluation of semantic and emotion of sentence text Each participant evaluated all 5,070 sentences to tell whether the sentences had emotional and semantic content. The emotional content was rated on a 9-point Likert scale. Point 1 represents negative emotions or feelings, point 5 means neutral, and 9 indicates positive emotions. In semantic evaluation, 0 means no semantics, while 1 represents semantics present. The mean value and standard deviation for each sentence were calculated based on emotional ratings from 10 raters. No meaningless sentences having a score out of mean ± 3*standard. To further ensure all the sentences were emotionally neutral, those with average scores out of 4 to 6 were also deleted (n = 7) (Cao et al., 2019). Sentences were removed if any semantics existed (n = 1915) Finally, 3148 sentences were included as the material in the recording session. Sentence Recording Participants Six native Mandarin speakers (mean age = 23.8 ± 2.73 years; three males and three females) with at least two years of broadcasting experience were recruited through social media. They had an educational year above 16 and did not have anxiety or depression (Costantini et al., 2021; Toussaint et al., 2020). Each speaker read all the sentences and was compensated ￥200 for their participation. Recording and encoding All 3148 sentences were digitally recorded in a soundproof room with a sampling rate of 48 kHz and a resolution of 16 bits, using a peripheral lavalier (Busquet et al., 2024). The microphone (Rode SmartLav+, Australia) was attached to the speakers’ clothing, about 15~20cm (6 inches) from the face. Speaker participants were asked not to change the distance or move the mic while recording to prevent any potential noise interference. Speakers were instructed to deliver the sentences in a neutral tone naturally, without emphasizing any specific words, and to maintain a constant speech level and rate throughout the recording session as much as possible. There would be a slight pause at the interval between two sentences to avoid any difficulty in the sentence-cutting process. If the speakers hesitated or made a noticeable error, they were requested to repeat the sentence. Once the recording session finished, the sound files were segmented using the pydub (AudioSegment in Python) package (Borse), and each sentence was saved as an individual WAV file. Subsequently, each audio file was checked by two researchers with the sentence text for accuracy and was also checked for any audible distortions, mic pops, clipping, or ambient sound. Files were deleted if there were any noise or mispronunciations of keywords. We used MATLAB R2023a to remove evident pauses and normalize the audio files to obtain a continuous and standardized stream (Feng et al., 2018). Parselmouth (Praat in Python) (Jadoul et al., 2018) package was used to extract the acoustic features of each audio file, including duration (in seconds); F0 mean (in Hz), F0 standard deviation (SD), F0 minimum, F0 maximum; harmonics-to-noise ratio (HNR); jitter (local); shimmer (local); intensity (in dB); root-mean-square (RMS) amplitude; spectral center of gravity (in Hz), and spectral spread (in Hz). Results Thirty-nine audios with pronunciation errors of keywords, and 29 audios with background noises were removed. The final database contained 18,820 voice files (please see the Supplementary Materials for details). Table 1 shows the mean values of acoustic features for each speaker. The duration ranges from 2.1 s to 2.53 s, the F0 mean is between 120.12 Hz and 237.14 Hz, the HNR varies from 9.15 dB to 14.83 dB, and the intensity is between 73.61 dB and 77.40 dB. The variations in jitter and shimmer are relatively small, ranging from 0.02 to 0.04 and 0.08 to 0.12, respectively. The spectral center of gravity ranges from 1014.79 Hz to 1763.25 Hz, and the range of spectral spread spans from 1231.34 Hz to 1530.20 Hz. Table 2 presents the results of the t-test analysis, showing significant differences in 12 acoustic features between male and female speakers (all p < 0.001). Apart from the parameters shimmer and jitter, the mean values of other features are higher in females than males. Table 1 Acoustic characteristics of the valid voices (n = 18,820). Speakers (n) # Duration (s) Pitch_F0 (Hz) HNR (dB) Jitter (local) Shimmer (local) Intensity (dB) RMS Energy (Amplitude) SpectralCOG (Hz) Spectralspread (Hz) Mean SD Min Max F1 (3131) 2.39 211.14 34.59 128.28 381.59 14.83 0.02 0.08 77.40 0.20 1261.45 1491.83 F2 (3144) 2.53 237.14 45.54 129.95 417.36 11.71 0.02 0.10 73.85 0.15 1763.25 1515.87 F3 (3124) 2.24 228.58 44.41 112.35 406.30 11.46 0.02 0.09 76.55 0.18 1521.97 1530.20 M1 (3137) 2.38 140.05 36.19 85.04 315.40 10.08 0.03 0.11 73.02 0.13 1534.19 1485.24 M2 (3142) 2.15 120.12 46.12 76.49 381.25 9.15 0.04 0.12 73.61 0.15 1380.09 1497.95 M3 (3142) 2.10 123.47 35.94 86.26 356.88 10.81 0.03 0.12 74.12 0.15 1014.79 1231.34 # Number of sounds; SD, standard deviation; COG, center of gravity; HNR, harmonics-to-noise ratio Table 2 Acoustic parameters of pseudo-sentences between female and male speakers Acoustic features Female Male t-value p-value Duration (s ± SD) 2.39 ± 0.26 2.21 ± 0.21 52.69 < 0.001 Pitch_F0 (Hz ± SD) Mean 225.64 ± 16.42 127.87 ± 14.72 430.1 < 0.001 SD 41.52 ± 9.27 39.42 ± 18.74 9.72 < 0.001 Min 123.55 ± 36.19 82.59 ± 6.59 108.1 < 0.001 Max 401.77 ± 105.77 351.19 ± 145.77 27.24 < 0.001 HNR (dB ± SD) 12.67 ± 2.21 10.01 ± 1.36 99.37 < 0.001 Jitter (local, mean ± SD) 0.02 ± 0 0.03 ± 0.01 -114 < 0.001 Shimmer (local, mean ± SD) 0.09 ± 0.01 0.12 ± 0.01 -125 < 0.001 Intensity (dB ± SD) 75.93 ± 2.13 73.58 ± 1.61 85.38 < 0.001 RMS Energy (mean ± SD) 0.18 ± 0.03 0.14 ± 0.02 79.09 < 0.001 Spectral COG (Hz ± SD) 1515.89 ± 306.16 1309.57 ± 290.06 47.46 < 0.001 Spectral spread (Hz ± SD) 1512.62 ± 129.91 1404.8 ± 168.68 49.12 < 0.001 SD, standard deviation; COG, center of gravity; HNR, harmonics-to-noise ratio. There are 9399 and 9421 audios for female and male speakers, respectively. Discussion This study created a Mandarin Chinese database of pseudo-sentences. It contained 3148 neutral sentences, each verified by ten native language users to ensure they have no semantic meaning or emotional content. These sentences were recorded by six native Mandarin Chinese speakers (three women and three men) with experience in hosting or broadcasting. Finally, the corpus included 18820 valid meaningless sentences. There were significant gender differences in acoustic characteristics, which was consistent with previous research (Ko et al., 2006 ). Differences in the physiological parameters of the vocal apparatus can result in variations in acoustic features (Wu et al., 1991 ). Therefore, we recommend considering sex differences in speech characteristics when using this corpus and select appropriate stimulus materials. This pseudo-sentence database could find applications across a wide range of applications in linguistics, cognitive science, psychology, rehabilitation medicine, et al. The most common utilization is as auditory materials to measure the capacities of speech perception in different populations, such as older adults (Feng et al., 2018 ; Humes et al., 1994 ), patients with hearing loss (Pittman & Schuett, 2013 ; das Neves et al., 2021 ), individuals with mental disorder, such as autism spectrum disorder (Alho et al., 2021 ) or schizophrenia (Wu et al., 2018 ), and students (Service et al., 2022 ), to investigate the neurocognitive mechanisms in auditory linguistic processing or language acquisition. Moreover, it could be materials to develop discrimination training to enhance participants’ auditory comprehension. Pseudo-sentences are also materials for evaluating speech prosody recognition due to the minimization of semantic meaning. Subjects rely on the prosodic information to identify the accurate types of emotions (Livingstone & Russo, 2018 ); experienced actors could record various emotional speeches with the sentence text of this corpus, thereby establishing a sufficient emotional speech database. The other usage of pseudo-sentences is visual cues. In reading tests, the sentence text could be stimuli to reveal the neural basis of linguistic predictions (Bonhage et al., 2015 ). Moreover, analyzing the orofacial movements of patients when reading nonsense sentences aloud could help assess the severity of stuttering (McClean et al., 2004 ). This study has several limitations. First, despite selecting high-frequency words from China Daily, some new buzzwords on the internet may appear in the pseudo sentences. Secondly, in the sentence construction procedure, we only removed reduplicated words, and sentences with repeated function words or sentences with the same characters in both function and content words were retained. These sentences are marked in the supplementary materials, and researchers can choose whether to use them based on their research objectives. Thirdly, we provided raw text and audio materials. Researchers and clinicians may need to process the audio files according to their study aims, such as adding noise masking or information masking. Conclusions This study presented the Mandarin Chinese auditory stimuli database, a set of standardized pseudo-sentences spoken in a neutral tone. The database consists of 3148 sentences of text and 18,820 voices recorded by six native Mandarin Chinese speakers. Each sentence is neutral and without semantics. Despite the limitations discussed above, our new sentence materials could be valuable for researchers and clinicians in various psycholinguistic studies for multiple test/training conditions or those needing a large number of stimuli. Declarations Acknowledgments We would like to thank the evaluators and speakers for their engagement in the study. Author Contributions Anqi Zhou: Investigation, Methodology, Analysis, Visualization, Writing - original draft, Writing - Reviewing and Editing. Qiuhong Li: Methodology, Investigation. Chao Wu: Conceptualization, Methodology, Writing - Reviewing and editing, Support and supervision. Funding This work was supported by the Natural Science Foundation of China (grant number: 32271138). Data availability The Mandarin Chinese Speech Database is available at the Open Science Framework (https://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f/). 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Speech communication , 49 (12), 892-904. https://doi.org/10.1016/j.specom.2007.05.005 Su, Y. E., & Naigles, L. R. (2019). Online Processing of Subject-Verb-Object Order in a Diverse Sample of Mandarin-Exposed Preschool Children with Autism Spectrum Disorder. Autism Res , 12 (12), 1829-1844. https://doi.org/10.1002/aur.2190 Sun, M., Chen, X., Zhang, K., Guo, Z., & Liu, Z. (2016). Thulac: An efficient lexical analyzer for chinese. Retrieved Jan , 10 , 2022. Feng, T., Chen, Q., & Xiao, Z. (2018). Age-Related Differences in the Effects of Masker Cuing on Releasing Chinese Speech From Informational Masking. Front Psychol , 9 , 1922. https://doi.org/10.3389/fpsyg.2018.01922 Helfer, K. S. (1997). Auditory and auditory-visual perception of clear and conversational speech. J Speech Lang Hear Res , 40 (2), 432-443. https://doi.org/10.1044/jslhr.4002.432 Costantini, L., Pasquarella, C., Odone, A., Colucci, M. E., Costanza, A., Serafini, G., Aguglia, A., Belvederi Murri, M., Brakoulias, V., Amore, M., Ghaemi, S. N., & Amerio, A. (2021). Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. J Affect Disord , 279 , 473-483. https://doi.org/10.1016/j.jad.2020.09.131 Toussaint, A., Hüsing, P., Gumz, A., Wingenfeld, K., Härter, M., Schramm, E., & Löwe, B. (2020). Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J Affect Disord , 265 , 395-401. https://doi.org/10.1016/j.jad.2020.01.032 Cao, Y., Yang, Y., & Wang, L. (2019). Concurrent emotional response and semantic unification: An event-related potential study. Cogn Affect Behav Neurosci , 19 (1), 154-164. https://doi.org/10.3758/s13415-018-00652-5 Busquet, F., Efthymiou, F., & Hildebrand, C. (2024). Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices. Behav Res Methods , 56 (3), 2114-2134. https://doi.org/10.3758/s13428-023-02139-9 Borse, K. Split audio files using silence detection in Python . https://www.codespeedy.com/split-audio-files-using-silence-detection-in-python/ Jadoul, Y., Thompson, B., & de Boer, B. (2018). Introducing Parselmouth: A Python interface to Praat. Journal of Phonetics , 71 , 1-15. https://doi.org/https://doi.org/10.1016/j.wocn.2018.07.001 Ko, S. J., Judd, C. M., & Blair, I. V. (2006). What the Voice Reveals: Within- and Between-Category Stereotyping on the Basis of Voice. Personality and Social Psychology Bulletin , 32 (6), 806-819. https://doi.org/doi: 10.1177/0146167206286627 Wu, K., Wu, K., & Childers, D. G. (1991). Gender recognition from speech. Part I: Coarse analysis. The Journal of the Acoustical Society of America , 90 (4), 1828-1840. https://doi.org/10.1121/1.401663 Humes, L. E., Watson, B. U., Christensen, L. A., Cokely, C. G., Halling, D. C., & Lee, L. (1994). Factors associated with individual differences in clinical measures of speech recognition among the elderly. J Speech Hear Res , 37 (2), 465-474. https://doi.org/10.1044/jshr.3702.465 Pittman, A. L., & Schuett, B. C. (2013). Effects of semantic and acoustic context on nonword detection in children with hearing loss. Ear Hear , 34 (2), 213-220. https://doi.org/10.1097/AUD.0b013e31826e5006 das Neves, A. J., Almeida-Verdu, A. C. M., do Nascimento Silva, L. T., Moret, A. L. M., & das Graças de Souza, D. (2021). Auditory sentence comprehension in children with cochlear implants after simple visual discrimination training with specific auditory-visual consequences. Learn Behav , 49 (2), 240-258. https://doi.org/10.3758/s13420-020-00435-4 Wu, C., Wang, C., & Li, L. (2018). Speech-on-speech masking and psychotic symptoms in schizophrenia. Schizophr Res Cogn , 12 , 37-39. https://doi.org/10.1016/j.scog.2018.02.005 Livingstone, S. R., & Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS One , 13 (5), e0196391. https://doi.org/10.1371/journal.pone.0196391 Bonhage, C. E., Mueller, J. L., Friederici, A. D., & Fiebach, C. J. (2015). Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension. Cortex , 68 , 33-47. https://doi.org/10.1016/j.cortex.2015.04.011 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Language Resources and Evaluation → Version 1 posted Editorial decision: Revision requested 13 Sep, 2024 Reviews received at journal 05 Sep, 2024 Reviews received at journal 27 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers invited by journal 26 Jul, 2024 Editor assigned by journal 26 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 07 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4702345","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":338973257,"identity":"fd353785-bd5c-4586-9e69-a38339c8040d","order_by":0,"name":"Anqi Zhou","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"Zhou","suffix":""},{"id":338973258,"identity":"9c2049c3-d46a-4a01-ba0f-ac4ba90f7aab","order_by":1,"name":"Qiuhong Li","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Qiuhong","middleName":"","lastName":"Li","suffix":""},{"id":338973260,"identity":"3648e4b6-562e-47bd-95ff-6e62c38e1550","order_by":2,"name":"Chao Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3QMQrCMBSA4UghLlHXBBU9QkqgXUSvogQ6iXMnKQi6iLvgIXqEQIYuca906ZRJdxfRZHGscRPMPzwyvA8eAcDn+82gHT3QzgL7aGWuhGRIfE3w3JHQkuv6nkpMLzfJEJgMcxHoupkkcbhXhlQrzhFIWC5gTJtIVC4h7myrNa2WTCIgF7lAEH8i5LGtzGHKkqcb6XcsKREzh4nPZKZ01B+oJyZqxcMT5ewoYdRIyI5rck0T3C3OEl/T6fBQbHQjeTcWyEz7VYHTvmmUIddVn8/n+7de7gdJojnqZ+4AAAAASUVORK5CYII=","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-07-08 03:36:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4702345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4702345/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10579-024-09790-4","type":"published","date":"2024-11-30T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382796,"identity":"35ba24c9-a0b3-461d-9ca0-6f06c52e45ec","added_by":"auto","created_at":"2024-12-02 16:31:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4702345/v1/fce2ecb5-f9f1-4f59-a4c3-97fd7bec552f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mandarin Chinese Speech Database: A Large Corpus for Auditory Neutral Nonsense Pseudo-Sentences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCorrectly perceiving speech contexts in communication is crucial in everyday life. However, it could be difficult for listeners to understand accurate information in noisy environments, like the Cocktail Party Effect (McDermott, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cherry, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1953\u003c/span\u003e). Interest in speech recognition has grown in recent years both in auditory research and clinical practice (Jones \u0026amp; Freyman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alho et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Materials assessing the ability to recognize speech include single words (Peterson \u0026amp; Lehiste, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1962\u003c/span\u003e; Wilson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Cheoy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), spondees (Zhang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Conn et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), and sentences (Bolia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nielsen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; O'Neill et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hagerman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Nuesse et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared to isolated words and spondees, sentences better represent the auditory scenarios encountered in real-life listening environments, and receiving sentence information requires a more comprehensive set of linguistic computation, thus providing insights into naturalistic speech processing (Alho et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rossell et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Moreover, in sentence recognition tasks, participants could respond to multiple keywords per trial, which enhances efficiency compared to single-word recognition tasks (Jett et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrequency, context, and length of speech stimuli are three dominating factors for word recognition accuracy (Grosjean, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Discriminating words with low frequency might confuse participants and lead to unreliable outcomes (Peterson \u0026amp; Lehiste, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). Using frequently used words could reduce the inconsistency in familiarity with the words among different listener groups (Calandruccio \u0026amp; Smiljanic, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Sentence length could increase linguistic complexity (van der Hoek-Snieders \u0026amp; Rhebergen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, several studies have developed closed-set sentences to control the length. For example, a study utilized Coordinate Response Measure (a call sign and a color-number combination) to construct sentences (Bolia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), while some use sentences structured as \u0026ldquo;name\u0026thinsp;+\u0026thinsp;verb\u0026thinsp;+\u0026thinsp;number\u0026thinsp;+\u0026thinsp;adjective\u0026thinsp;+\u0026thinsp;noun\u0026rdquo; as stimuli (Hagerman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Kroll et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, the vocabulary used in these structured sentences is limited, restricting listeners\u0026rsquo; response options and reducing the effectiveness of tests (Nielsen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The factor of context has also aroused increasing interest in recent years. Based on the presence or absence of syntactic and semantic context, sentence corpora developed in previous studies have two categories: meaningful and meaningless sentences. The former are sentences with correct grammatical structure and semantic information, such as AzBio sentence lists (Spahr et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the Bamford-Kowal-Bench British sentence test (Bench et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), grounded in daily conversations and ideas. Another type is pseudo-sentence with syntactic structure but without lexical meaning (Tao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Compared with meaningless sentences, keyword recognition improves when using semantic sentences to demonstrate words (Zhang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Van Engen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kalikow et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Meaningful sentences are highly predictable since context would give cues and limit the vocabulary pool of possible keywords, requiring less capacity of listeners to understand audible acoustic-phonetic information for speech recognition (Wasiuk et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the daily listening environment with multi-talkers, pseudo-sentences are commonly used speech stimuli because of their abstract nature and low predictability. Pseudo-sentences make listeners focus on the acoustic properties of the speech rather than the sentence meanings (Steiner et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the unpredictability of words in pseudo-sentences reduces the effect of long-term memory or prior knowledge of listeners (das Gra\u0026ccedil;as de Souza et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to serving as stimuli for speech perception, meaningless sentences are materials used to train or measure participants\u0026rsquo; abilities in emotional recognition, reading, orofacial movements, and short-term memory (Service et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zupan \u0026amp; Eskritt, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McClean et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To our knowledge, there were few Mandarin Chinese databases containing pseudo-sentences. Three studies used pseudo-sentences to assess the emotion discrimination ability of participants (Gong et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu \u0026amp; Pell, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Paulmann \u0026amp; Uskul, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Two databases contained sentences created by researchers without providing detailed information (Liu \u0026amp; Pell, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Paulmann \u0026amp; Uskul, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Two studies did not control the emotional (positive, negative, or neutral) valence expressed in neutral sentences (Paulmann \u0026amp; Uskul, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The number of meaningless sentences included in the above databases is also limited.\u003c/p\u003e \u003cp\u003eIn this study, we developed and validated a large auditory nonsense sentence corpus, built on the commonly used subject-verb-object (SVO) order in Mandarin Chinese (Su \u0026amp; Naigles, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and matched to the existing sentence database in terms of the number of keywords, and sentence length (Gong et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). All the keywords were high-frequency disyllable in news reports, and each appeared in a sentence only once. Besides, we conducted a pilot study to ensure all sentences were meaningless and neutral. Then, we invited six native speakers (three males and three females) with broadcasting experience to record the sentences in a neutral tone. Finally, we performed energy balance and standardization on these sentences.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSentence Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeywords (subject, verb, and object) in the nonsense sentences were disyllabic words, based on the articles from China Daily by February 25, 2024. THULAC (in Python) package (Sun et al., 2016) was used to segment words and mark their parts of speech. The noun and verb list were ranked according to the frequency of word occurrence, containing 49,249 nouns and 33,871 verbs without duplicate words. Two researchers checked the word lists, removing reduplicative words, words containing English letters, or words with verb-object structure and obtaining 45,634 nouns and 22,171 verbs. A total of 12,140 nouns and 6,070 verbs with a high frequency of occurrence were randomly combined into 6070 syntactically but not semantically correct sentences with the frame of subject (noun) + predicate (verb) + object (noun). Next, a double-syllable pronoun was inserted before each noun, and an auxiliary word was inserted before the verb in a sentence to achieve a phonetic balance (Yang et al., 2007; Feng et al., 2018). Accordingly, the entire sentence consisted of 12 Chinese characters (pronoun + subject + adverbial + predicate + pronoun + object), and the word-by-word English translation of it was similar but not identical to the English meaningless sentences constructed by Helfer and Rossell (Rossell et al., 1998; Helfer, 1997). For example, one translated pseudo-sentence \u0026ldquo;他的甲烷可能传播两个皮带\u0026rdquo; was \u0026ldquo;His \u003cu\u003emethane\u003c/u\u003e can \u003cu\u003ebroadcast\u003c/u\u003e two \u003cu\u003ebelts\u003c/u\u003e,\u0026rdquo; where the keywords are underlined. Among the 6070 sentences, 1000 were removed because they contained rare characters, semantics, or emotions. Finally, 5070 sentences remained for the preprocessing step before recording.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing of Sentence Text\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen native Mandarin speakers (five females and five males; mean age = 24.7 \u0026plusmn; 3.7 years) were recruited through online advertisement. Each participant completed sociodemographic information, the Chinese version of the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the\u0026nbsp;nine-item Patient Health Questionnaire Depression Scale\u0026nbsp;(PHQ-9). Their\u0026nbsp;PHQ-9 and GAD-6\u0026nbsp;scores were all less than 10\u0026nbsp;(Costantini et al., 2021; Toussaint et al., 2020), and their years of education were\u0026nbsp;all above 16.\u0026nbsp;Each participant was compensated\u0026nbsp;￥100\u0026nbsp;for their engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of semantic and emotion of sentence text\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant evaluated all 5,070 sentences to tell whether the sentences had emotional and semantic content. The emotional content was rated on a 9-point Likert scale. Point 1 represents negative emotions or feelings, point 5 means neutral, and 9 indicates positive emotions. In semantic evaluation, 0 means no semantics, while 1 represents semantics present.\u003c/p\u003e\n\u003cp\u003eThe mean value and standard deviation for each sentence were calculated based on emotional ratings from 10 raters. No meaningless sentences having a score out of mean \u0026plusmn; 3*standard. To further ensure all the sentences were emotionally neutral, those with average scores out of 4 to 6 were also deleted (n = 7) (Cao et al., 2019). Sentences were removed if any semantics existed (n = 1915) Finally, 3148 sentences were included as the material in the recording session.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSentence Recording\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix native Mandarin speakers (mean age = 23.8 \u0026plusmn; 2.73 years; three males and three females) with at least two years of broadcasting experience were recruited through social media. They had an educational year above 16 and did not have anxiety or depression (Costantini et al., 2021; Toussaint et al., 2020). Each speaker read all the sentences and was compensated ￥200 for their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecording and encoding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All 3148 sentences were digitally recorded in a soundproof room with a sampling rate of 48 kHz and a resolution of 16 bits, using a peripheral lavalier \u0026nbsp;(Busquet et al., 2024). The microphone (Rode SmartLav+, Australia) was attached to the speakers\u0026rsquo; clothing, about 15~20cm (6 inches) from the face. Speaker participants were asked not to change the distance or move the mic while recording to prevent any potential noise interference. Speakers were instructed to deliver the sentences in a neutral tone naturally, without emphasizing any specific words, and to maintain a constant speech level and rate throughout the recording session as much as possible. There would be a slight pause at the interval between two sentences to avoid any difficulty in the sentence-cutting process. If the speakers hesitated or made a noticeable error, they were requested to repeat the sentence.\u003c/p\u003e\n\u003cp\u003eOnce the recording session finished, the sound files were segmented using the pydub (AudioSegment in Python) package (Borse), and each sentence was saved as an individual WAV file. Subsequently, each audio file was checked by two researchers with the sentence text for accuracy and was also checked for any audible distortions, mic pops, clipping, or ambient sound. Files were deleted if there were any noise or mispronunciations of keywords. We used MATLAB R2023a to remove evident pauses and normalize the audio files to obtain a continuous and standardized stream (Feng et al., 2018).\u003c/p\u003e\n\u003cp\u003eParselmouth (Praat in Python) (Jadoul et al., 2018) package was used to extract the acoustic features of each audio file, including duration (in seconds); F0 mean (in Hz), F0 standard deviation (SD), F0 minimum, F0 maximum; harmonics-to-noise ratio (HNR); jitter (local); shimmer (local); intensity (in dB); root-mean-square (RMS) amplitude; spectral center of gravity (in Hz), and spectral spread (in Hz).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThirty-nine audios with pronunciation errors of keywords, and 29 audios with background noises were removed. The final database contained 18,820 voice files (please see the Supplementary Materials for details). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the mean values of acoustic features for each speaker. The duration ranges from 2.1 s to 2.53 s, the F0 mean is between 120.12 Hz and 237.14 Hz, the HNR varies from 9.15 dB to 14.83 dB, and the intensity is between 73.61 dB and 77.40 dB. The variations in jitter and shimmer are relatively small, ranging from 0.02 to 0.04 and 0.08 to 0.12, respectively. The spectral center of gravity ranges from 1014.79 Hz to 1763.25 Hz, and the range of spectral spread spans from 1231.34 Hz to 1530.20 Hz. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the t-test analysis, showing significant differences in 12 acoustic features between male and female speakers (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Apart from the parameters shimmer and jitter, the mean values of other features are higher in females than males.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcoustic characteristics of the valid voices (n\u0026thinsp;=\u0026thinsp;18,820).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpeakers\u003c/p\u003e \u003cp\u003e(n)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration (s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePitch_F0 (Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHNR (dB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJitter (local)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShimmer (local)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntensity (dB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRMS Energy (Amplitude)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpectralCOG\u003c/p\u003e \u003cp\u003e(Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpectralspread (Hz)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1 (3131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e381.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1261.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1491.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2 (3144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e417.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1763.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1515.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3 (3124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e406.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e76.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1521.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1530.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1 (3137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e315.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1534.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1485.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2 (3142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e381.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1380.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1497.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3 (3142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e74.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1014.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1231.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e#\u003c/sup\u003e Number of sounds; SD, standard deviation; COG, center of gravity; HNR, harmonics-to-noise ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcoustic parameters of pseudo-sentences between female and male speakers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcoustic features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (s\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePitch_F0 (Hz\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225.64\u0026thinsp;\u0026plusmn;\u0026thinsp;16.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.42\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.55\u0026thinsp;\u0026plusmn;\u0026thinsp;36.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.59\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e401.77\u0026thinsp;\u0026plusmn;\u0026thinsp;105.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351.19\u0026thinsp;\u0026plusmn;\u0026thinsp;145.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHNR (dB\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJitter (local, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShimmer (local, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity (dB\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMS Energy (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral\u003csub\u003eCOG\u003c/sub\u003e (Hz\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1515.89\u0026thinsp;\u0026plusmn;\u0026thinsp;306.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1309.57\u0026thinsp;\u0026plusmn;\u0026thinsp;290.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral\u003csub\u003espread\u003c/sub\u003e (Hz\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1512.62\u0026thinsp;\u0026plusmn;\u0026thinsp;129.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1404.8\u0026thinsp;\u0026plusmn;\u0026thinsp;168.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSD, standard deviation; COG, center of gravity; HNR, harmonics-to-noise ratio.\u003c/p\u003e \u003cp\u003eThere are 9399 and 9421 audios for female and male speakers, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study created a Mandarin Chinese database of pseudo-sentences. It contained 3148 neutral sentences, each verified by ten native language users to ensure they have no semantic meaning or emotional content. These sentences were recorded by six native Mandarin Chinese speakers (three women and three men) with experience in hosting or broadcasting. Finally, the corpus included 18820 valid meaningless sentences. There were significant gender differences in acoustic characteristics, which was consistent with previous research (Ko et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Differences in the physiological parameters of the vocal apparatus can result in variations in acoustic features (Wu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Therefore, we recommend considering sex differences in speech characteristics when using this corpus and select appropriate stimulus materials.\u003c/p\u003e \u003cp\u003eThis pseudo-sentence database could find applications across a wide range of applications in linguistics, cognitive science, psychology, rehabilitation medicine, et al. The most common utilization is as auditory materials to measure the capacities of speech perception in different populations, such as older adults (Feng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Humes et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), patients with hearing loss (Pittman \u0026amp; Schuett, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; das Neves et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), individuals with mental disorder, such as autism spectrum disorder (Alho et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or schizophrenia (Wu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and students (Service et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), to investigate the neurocognitive mechanisms in auditory linguistic processing or language acquisition. Moreover, it could be materials to develop discrimination training to enhance participants\u0026rsquo; auditory comprehension. Pseudo-sentences are also materials for evaluating speech prosody recognition due to the minimization of semantic meaning. Subjects rely on the prosodic information to identify the accurate types of emotions (Livingstone \u0026amp; Russo, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); experienced actors could record various emotional speeches with the sentence text of this corpus, thereby establishing a sufficient emotional speech database. The other usage of pseudo-sentences is visual cues. In reading tests, the sentence text could be stimuli to reveal the neural basis of linguistic predictions (Bonhage et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, analyzing the orofacial movements of patients when reading nonsense sentences aloud could help assess the severity of stuttering (McClean et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, despite selecting high-frequency words from China Daily, some new buzzwords on the internet may appear in the pseudo sentences. Secondly, in the sentence construction procedure, we only removed reduplicated words, and sentences with repeated function words or sentences with the same characters in both function and content words were retained. These sentences are marked in the supplementary materials, and researchers can choose whether to use them based on their research objectives. Thirdly, we provided raw text and audio materials. Researchers and clinicians may need to process the audio files according to their study aims, such as adding noise masking or information masking.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study presented the Mandarin Chinese auditory stimuli database, a set of standardized pseudo-sentences spoken in a neutral tone. The database consists of 3148 sentences of text and 18,820 voices recorded by six native Mandarin Chinese speakers. Each sentence is neutral and without semantics. Despite the limitations discussed above, our new sentence materials could be valuable for researchers and clinicians in various psycholinguistic studies for multiple test/training conditions or those needing a large number of stimuli.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the evaluators and speakers for their engagement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnqi Zhou: Investigation, Methodology, Analysis, Visualization, Writing - original draft, Writing - Reviewing and Editing. Qiuhong Li: Methodology, Investigation. Chao Wu: Conceptualization, Methodology, Writing - Reviewing and editing, Support and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (grant number: 32271138).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mandarin Chinese Speech Database is available at the Open Science Framework (https://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlease see the Open Science Framework (https://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcDermott, J. 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Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension. \u003cem\u003eCortex\u003c/em\u003e,\u003cem\u003e 68\u003c/em\u003e, 33-47. https://doi.org/10.1016/j.cortex.2015.04.011 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"language-resources-and-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lrev","sideBox":"Learn more about [Language Resources and Evaluation](http://link.springer.com/journal/10579)","snPcode":"10579","submissionUrl":"https://submission.nature.com/new-submission/10579/3","title":"Language Resources and Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Speech Perception, Chinese meaningless sentences, Voices, Acoustic features, Neutral stimuli","lastPublishedDoi":"10.21203/rs.3.rs-4702345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4702345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWord frequency, context, and length are three core elements that impact speech perception. Considering the limitations of previous Chinese stimulus databases, such as non-standardized sentence structures, uncontrolled emotional information that may exist in semantics, and a relatively small number of voice items, we developed an abundant and reliable Chinese Mandarin nonsense pseudo-sentences database with fixed syntax (pronoun\u0026thinsp;+\u0026thinsp;subject\u0026thinsp;+\u0026thinsp;adverbial\u0026thinsp;+\u0026thinsp;predicate\u0026thinsp;+\u0026thinsp;pronoun\u0026thinsp;+\u0026thinsp;object), lengths (6 two-character words), and high-frequency words in daily life. The high-frequency keywords (subject, predicate, and object) were extracted from China Daily. Ten native Chinese participants (five women and five men) evaluated the sentences. After removing sentences with potential emotional and semantic content valence, 3,148 meaningless neutral sentence text remained. The sentences were recorded by six native speakers (three males and three females) with broadcasting experience in a neutral tone. After examining and standardizing all the voices, 18,820 audio files were included in the corpus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f\u003c/span\u003e\u003cspan address=\"https://osf.io/ra3gm/?view_only=98c3b6f1ee7747d3b3bcd60313cf395f\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For each speaker, 12 acoustic parameters (duration, F0 mean, F0 standard deviation, F0 minimum, F0 maximum, harmonics-to-noise ratio, jitter, shimmer, in-tensity, root-mean-square amplitude, spectral center of gravity, and spectral spread) were retrieved, and there were significant gender differences in the acoustic features (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This database could be valuable for researchers and clinicians to investigate rich topics, such as children\u0026rsquo;s reading ability, speech recognition abilities in different populations, and oral cues for orofacial movement training in stutterers.\u003c/p\u003e","manuscriptTitle":"The Mandarin Chinese Speech Database: A Large Corpus for Auditory Neutral Nonsense Pseudo-Sentences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 06:44:29","doi":"10.21203/rs.3.rs-4702345/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-13T10:31:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-05T23:05:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-27T09:10:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254388033887559436624687060650190309145","date":"2024-07-29T06:58:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22861447765877083348368269841447404452","date":"2024-07-28T13:59:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-26T13:55:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-26T13:29:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T07:14:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Language Resources and Evaluation","date":"2024-07-08T03:34:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"language-resources-and-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lrev","sideBox":"Learn more about [Language Resources and Evaluation](http://link.springer.com/journal/10579)","snPcode":"10579","submissionUrl":"https://submission.nature.com/new-submission/10579/3","title":"Language Resources and Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1f514b51-d371-453e-a405-b26ecdac2b03","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:03:28+00:00","versionOfRecord":{"articleIdentity":"rs-4702345","link":"https://doi.org/10.1007/s10579-024-09790-4","journal":{"identity":"language-resources-and-evaluation","isVorOnly":false,"title":"Language Resources and Evaluation"},"publishedOn":"2024-11-30 15:57:55","publishedOnDateReadable":"November 30th, 2024"},"versionCreatedAt":"2024-08-22 06:44:29","video":"","vorDoi":"10.1007/s10579-024-09790-4","vorDoiUrl":"https://doi.org/10.1007/s10579-024-09790-4","workflowStages":[]},"version":"v1","identity":"rs-4702345","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4702345","identity":"rs-4702345","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}