A Contrastive Learning-Based Short Speech Bio-key Generation Model

A Contrastive Learning-Based Short Speech Bio-key Generation Model | 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 Research Article A Contrastive Learning-Based Short Speech Bio-key Generation Model Zhengyin Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9245518/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract With the widespread deployment of biometric authentication in critical security applications, bio-key generation technology has attracted increasing attention. The extraction of secure, stable, and high-entropy keys from biometric traits has become a central research challenge. However, existing bio-key generation methods fail to fully satisfy security requirements in practical scenarios, as the generated keys are vulnerable to attacks and may even lead to leakage of sensitive biometric information, resulting in unpredictable consequences. Moreover, variations in voiceprint acquisition conditions, noise interference, and the inherent instability of voiceprints make it difficult for current methods to achieve an effective balance between key consistency and security. To address these challenges , this paper proposes a short-speech bio-key generation model based on contrastive learning. The proposed model introduces a contrastive learning mechanism to align features across multiple samples of the same voiceprint while enhancing the discriminability between different voiceprints, thereby obtaining robust and highly discriminative voice representations. On this basis, feature quantization and error-correcting codes are employed to generate stable bio-key with high randomness. The proposed model consists of three main modules: the voiceprint information preprocessor, the voiceprint feature vector extrac-tor, and the voiceprint key fuzzy extractor. The experimental evaluation in this study was conducted on a self-collected dataset, namely GUIT VP01. All experimental results were obtained through comprehensive testing and performance validation on this dataset. Experimental results show that the proposed model generates keys with an entropy exceeding 1024 bits, an accuracy > 99%, and a misrecognition rate < 0.01%. These results demonstrate that the proposed voiceprint bio-key generation model effectively meets user requirements for both key strength and security. Bio-key Voiceprint Key Contrastive Learning Deep Learning Fuzzy Extractor Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 27 Mar, 2026 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. 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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-9245518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614699219,"identity":"d1be3e08-154c-42e0-84c0-fcf69f1c2d46","order_by":0,"name":"Zhengyin Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACPgYGNjCDjYH5AAODAYiZgF8LG0ILG1CpgQEJWhgYeEDKidEikf7swccdtYl90j2fX/MU/GHgZ88xYPi5A5+WhHTDmWeOJ7bJnN1mzQN0mGTPGwPG3jN4tRyT5m07ltgmkbvNGKTF4EaOATNjGz4tiW3Sf8Facp6BtdgT1pLMJs3YVgPSwvwYbIsEIS08z9gke9sOGLdJpJkxzjEw5pE486zgYC8eLfzs6c8kfrbVyc6fkfz4w5s/cnL87ckbH/zEowUKDjs2gBwJZPGAuAcIamBgqLMHEswfiFA5CkbBKBgFIxAAAOVOSZNwDaLnAAAAAElFTkSuQmCC","orcid":"","institution":"Guilin Institute of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhengyin","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2026-03-27 13:55:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9245518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9245518/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093692,"identity":"376dd164-e0b2-45e0-94c5-4c69ff14da5d","added_by":"auto","created_at":"2026-04-03 11:38:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1091468,"visible":true,"origin":"","legend":"","description":"","filename":"voicenpl.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9245518/v1_covered_37013586-2b9f-4735-a8c6-7a1310ee4ff4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Contrastive Learning-Based Short Speech Bio-key Generation Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neural-processing-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nepl","sideBox":"Learn more about [Neural Processing Letters](http://link.springer.com/journal/11063)","snPcode":"11063","submissionUrl":"https://submission.nature.com/new-submission/11063/3","title":"Neural Processing Letters","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bio-key, Voiceprint Key, Contrastive Learning, Deep Learning, Fuzzy Extractor","lastPublishedDoi":"10.21203/rs.3.rs-9245518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9245518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the widespread deployment of biometric authentication in critical security applications, bio-key generation technology has attracted increasing attention. The extraction of secure, stable, and high-entropy keys from biometric traits has become a central research challenge. However, existing bio-key generation methods fail to fully satisfy security requirements in practical scenarios, as the generated keys are vulnerable to attacks and may even lead to leakage of sensitive biometric information, resulting in unpredictable consequences. Moreover, variations in voiceprint acquisition conditions, noise interference, and the inherent instability of voiceprints make it difficult for current methods to achieve an effective balance between key consistency and security. To address these challenges , this paper proposes a short-speech bio-key generation model based on contrastive learning. The proposed model introduces a contrastive learning mechanism to align features across multiple samples of the same voiceprint while enhancing the discriminability between different voiceprints, thereby obtaining robust and highly discriminative voice representations. On this basis, feature quantization and error-correcting codes are employed to generate stable bio-key with high randomness. The proposed model consists of three main modules: the voiceprint information preprocessor, the voiceprint feature vector extrac-tor, and the voiceprint key fuzzy extractor. The experimental evaluation in this study was conducted on a self-collected dataset, namely GUIT VP01. All experimental results were obtained through comprehensive testing and performance validation on this dataset. Experimental results show that the proposed model generates keys with an entropy exceeding 1024 bits, an accuracy \u003e 99%, and a misrecognition rate \u003c 0.01%. These results demonstrate that the proposed voiceprint bio-key generation model effectively meets user requirements for both key strength and security.","manuscriptTitle":"A Contrastive Learning-Based Short Speech Bio-key Generation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 19:06:48","doi":"10.21203/rs.3.rs-9245518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T07:14:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160110330786928763748329428577038995810","date":"2026-04-21T00:25:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-30T15:15:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T13:37:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T01:39:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neural Processing Letters","date":"2026-03-27T13:44:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neural-processing-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nepl","sideBox":"Learn more about [Neural Processing Letters](http://link.springer.com/journal/11063)","snPcode":"11063","submissionUrl":"https://submission.nature.com/new-submission/11063/3","title":"Neural Processing Letters","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"82339986-57ad-45da-ad99-bcb02af23506","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T07:14:19+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T19:06:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 19:06:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9245518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9245518","identity":"rs-9245518","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}