Speaker Discrimination Using Long-Term Spectrum of Speech
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Date
2019-09-25
Authors
Sigmund, Milan
ORCID
0000-0003-3973-3626Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Kaunas University of Technology
Altmetrics
Abstract
In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.
In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.
In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.
Description
Citation
Information Technology and Control. 2019, vol. 48, issue 3, p. 446-453.
http://itc.ktu.lt/index.php/ITC/article/view/21248
http://itc.ktu.lt/index.php/ITC/article/view/21248
Document type
Peer-reviewed
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Published version
Date of access to the full text
Language of document
en