Improving isolated word recognition rates using multiple common vectors and a majority vote algorithm

Improving isolated word recognition rates using multiple common vectors and a majority vote algorithm | 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 Improving isolated word recognition rates using multiple common vectors and a majority vote algorithm Serkan KESER This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4514114/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Common Vector Approach (CVA) is a subspace classifier with significant success in isolated word recognition. However, when sufficient data is available, mixing the intra-class and inter-class subspaces can reduce recognition rates. To overcome this problem, a method is proposed in which Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GTCC), and i -vector features obtained from samples of classes in the database are divided into equal-sized clusters. This method reduces the number of features per class, thereby minimizing the mixing between intra-class and inter-class subspaces. Recognition is performed using the K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and CVA classifiers by using Multiple Common Vectors (MCV) obtained per class as feature vectors. By using projection matrices, a test signal is assigned to k classes, and the most suitable class is selected with the proposed Majority Vote Algorithm (MVA). Thus, the probability of assigning a test signal, which is misclassified in classical CVA, to the correct class is increased with the proposed method. The results obtained are higher than those found with the classical CVA method. This study demonstrates the potential and superiority of the new method proposed for isolated word recognition. The developed method provides a more efficient and effective recognition system by increasing classification accuracy compared to the classical CVA. Isolated word recognition MVA MCV CVA MFCC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4514114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312887616,"identity":"f2c1e70e-7122-4d1f-ab0f-74dfa36e5c55","order_by":0,"name":"Serkan KESER","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACHjBpw2DADBdKIEpLGpqWA4S1HGYwQAgR0CLfc/jwh497zsubszMfk66oucPAz55jwPxxD24tBmfb0iRnPLttuLOZLU3yzLFnDJI9bwwYDjzDo4Wfx4yZ58Btxg2HeYwNG9iALryRA9SCx2Xy/fyfP/85cM5+w2H+z4YN/w4z2BPSwnC2h0EaqCIRaAvjw8Y2oC0SBLQYnDlmJtlzIDl5w2E2w4eNfYd5JM48KzhwBp/DepIff/hxwM52w/nDDw42fDssx9+evPFBBT6HoQNwPJGiYRSMglEwCkYBFgAAdGVY0LrWluoAAAAASUVORK5CYII=","orcid":"","institution":"Kirsehir Ahi Evran University","correspondingAuthor":true,"prefix":"","firstName":"Serkan","middleName":"","lastName":"KESER","suffix":""}],"badges":[],"createdAt":"2024-06-01 14:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4514114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4514114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59818410,"identity":"927c6ce4-2706-4d70-a34b-fd3f42e60b55","added_by":"auto","created_at":"2024-07-08 02:41:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":383473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsvip.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4514114/v1_covered_b1fe45b4-113c-4126-8893-80233cac0fff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving isolated word recognition rates using multiple common vectors and a majority vote algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Isolated word recognition, MVA, MCV, CVA, MFCC","lastPublishedDoi":"10.21203/rs.3.rs-4514114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4514114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Common Vector Approach (CVA) is a subspace classifier with significant success in isolated word recognition. 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