Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics

Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Expert Systems This is a preprint and has not been peer reviewed. Data may be preliminary. 17 April 2025 V1 Latest version Share on Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics Authors : Ghada Alhussein 0000-0001-6181-8306 [email protected] , Mohanad Alkhodari 0000-0002-5248-6327 , Shiza Saleem , Ahsan H. Khandoker 0000-0002-0636-1646 , and Leontios J. Hadjileontiadis Authors Info & Affiliations https://doi.org/10.22541/au.174488617.72324998/v1 Published Expert Systems Version of record Peer review timeline 225 views 182 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The growing availability and variety of conversational data on multiple platforms have sparked a rising interest in dynamic emotion recognition.Speech plays a crucial role in establishing a dynamic emotional climate (EC) during peer conversation. In this study, we introduce a novel approach, DeepBispec, which utilizes deep bispectral conversational speech processing to extract features that can be utilized for predicting the emotions expressed during the conversation. Incorporating bispectrum images into a CNN model, DeepBispec extracts deep features, combines them with Affect dynamics (AD), and conducts EC classification.Three open data-sets proposed,i.e., K-EmoCon, IEMOCAP, and SEWA were used to test and cross-validate on DeepBispec in terms of EC arousal/valence level classification. The experimental results have shown that combining deep features with AD enhanced the performance of DeepBispec from an accuracy of 79% for EC arousal to 81.4% with AD and an average accuracy of 76.8% for EC valence to 77.5% with AD for K-EmoCon. IEMOCAP data-set displays similar trend with an average accuracy of 77.2% for arousal increasing to 79.6% and an increase from 65.7% to 73.6% for valance. The results show that the proposed approach outperforms other state-of-the-art approaches, including deep learning architectures like CNN and LSTM, in the domain of speech-based emotion recognition.The Bispectrum-based features capture the emotional content of the voice signal in a unique manner that conventional DL models do not achieve. Supplementary Material File (deepbispec__expert_systems (1).pdf) Download 4.73 MB Information & Authors Information Version history V1 Version 1 17 April 2025 Peer review timeline Published Expert Systems Version of Record 23 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Expert Systems Keywords affect dynamics bispectrum conversational emotion recognition convolution neural network deepbispec emotion climate Authors Affiliations Ghada Alhussein 0000-0001-6181-8306 [email protected] Khalifa University View all articles by this author Mohanad Alkhodari 0000-0002-5248-6327 Khalifa University View all articles by this author Shiza Saleem King's College London Faculty of Life Sciences & Medicine View all articles by this author Ahsan H. Khandoker 0000-0002-0636-1646 Khalifa University View all articles by this author Leontios J. Hadjileontiadis Khalifa University View all articles by this author Metrics & Citations Metrics Article Usage 225 views 182 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ghada Alhussein, Mohanad Alkhodari, Shiza Saleem, et al. Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics. Authorea . 17 April 2025. DOI: https://doi.org/10.22541/au.174488617.72324998/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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