Speaker verification with probability measurements

The usual application of speaker verification (SV) system is based on a Yes/No question, Are the speakers of these two signals the same? and on the assumption that cooperative users speak prompted words of phrases for comparison. However, there are cases when such constraints are not possible. Consi...

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Main Author: Vinluan, Anthony S.
Format: text
Language:English
Published: Animo Repository 2007
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/2154
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-31542021-10-25T02:51:36Z Speaker verification with probability measurements Vinluan, Anthony S. The usual application of speaker verification (SV) system is based on a Yes/No question, Are the speakers of these two signals the same? and on the assumption that cooperative users speak prompted words of phrases for comparison. However, there are cases when such constraints are not possible. Consider a scenario where there is a need to determine the likelihood of a suspected person being or not being the speaker of a speech recording. In such cases, the speaker verification system should be able to work even without explicit user cooperation and independent of the spoken words or phrases. This study aims to develop a text-independent speaker verification system that will be able to identify whether the speaker of two speech signals are the same or not. The speech feature extraction method used in this study is Linear Predictive Coding (LPC). The Euclidean Distance of the LPC coefficients is used to measure the degree of similarity between the two signals. A threshold value whether the two signals are similar (less than 0.5) or not (greater than 0.5) is utilized. The average Euclidean Distance between the speaker from the reference signal and target speaker from the test signal has an average of 0.4383, which means that they are the same speakers. This value was compared to two other values obtained from a different female speaker and from a male speaker, which is 0.5877 and 0.6119, respectively. Comparisons with the two other speakers yielded results greater than o.5 which means that they are not similar. The method is further validated using 20 speakers (10 males and 10 females). The False Rejection Rate and False Acceptance Rates are acquired. The average accuracy rates for male speakers are 89.45% and 84.80% for female speakers. 2007-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/2154 Bachelor's Theses English Animo Repository Speech processing systems Automatic speech recognition Speech perception Voice Voice frequency Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Speech processing systems
Automatic speech recognition
Speech perception
Voice
Voice frequency
Computer Sciences
spellingShingle Speech processing systems
Automatic speech recognition
Speech perception
Voice
Voice frequency
Computer Sciences
Vinluan, Anthony S.
Speaker verification with probability measurements
description The usual application of speaker verification (SV) system is based on a Yes/No question, Are the speakers of these two signals the same? and on the assumption that cooperative users speak prompted words of phrases for comparison. However, there are cases when such constraints are not possible. Consider a scenario where there is a need to determine the likelihood of a suspected person being or not being the speaker of a speech recording. In such cases, the speaker verification system should be able to work even without explicit user cooperation and independent of the spoken words or phrases. This study aims to develop a text-independent speaker verification system that will be able to identify whether the speaker of two speech signals are the same or not. The speech feature extraction method used in this study is Linear Predictive Coding (LPC). The Euclidean Distance of the LPC coefficients is used to measure the degree of similarity between the two signals. A threshold value whether the two signals are similar (less than 0.5) or not (greater than 0.5) is utilized. The average Euclidean Distance between the speaker from the reference signal and target speaker from the test signal has an average of 0.4383, which means that they are the same speakers. This value was compared to two other values obtained from a different female speaker and from a male speaker, which is 0.5877 and 0.6119, respectively. Comparisons with the two other speakers yielded results greater than o.5 which means that they are not similar. The method is further validated using 20 speakers (10 males and 10 females). The False Rejection Rate and False Acceptance Rates are acquired. The average accuracy rates for male speakers are 89.45% and 84.80% for female speakers.
format text
author Vinluan, Anthony S.
author_facet Vinluan, Anthony S.
author_sort Vinluan, Anthony S.
title Speaker verification with probability measurements
title_short Speaker verification with probability measurements
title_full Speaker verification with probability measurements
title_fullStr Speaker verification with probability measurements
title_full_unstemmed Speaker verification with probability measurements
title_sort speaker verification with probability measurements
publisher Animo Repository
publishDate 2007
url https://animorepository.dlsu.edu.ph/etd_bachelors/2154
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