HMM Speaker Identification Using Linear and Non-linear Merging Techniques

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dc.creatorMahola, Unathi
dc.creatorNelwamondo, Fulufhelo V.
dc.creatorMarwala, Tshilidzi
dc.date2007-05-11
dc.date.accessioned2026-07-07T08:00:55Z
dc.date.available2026-07-07T08:00:55Z
dc.descriptionSpeaker identification is a powerful, non-invasive and in-expensive biometric technique. The recognition accuracy, however, deteriorates when noise levels affect a specific band of frequency. In this paper, we present a sub-band based speaker identification that intends to improve the live testing performance. Each frequency sub-band is processed and classified independently. We also compare the linear and non-linear merging techniques for the sub-bands recognizer. Support vector machines and Gaussian Mixture models are the non-linear merging techniques that are investigated. Results showed that the sub-band based method used with linear merging techniques enormously improved the performance of the speaker identification over the performance of wide-band recognizers when tested live. A live testing improvement of 9.78% was achieved
dc.description6 pages
dc.identifierhttps://arxiv.org/abs/0705.1585
dc.identifierhttp://arxiv.org/abs/0705.1585
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128796
dc.subjectMachine Learning
dc.titleHMM Speaker Identification Using Linear and Non-linear Merging Techniques
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