Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models

Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models

This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral...

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Main Author: Magsino, Elmer R.
Format: text
Published: Animo Repository 2020
Subjects:
Automatic speech recognition
Regression analysis
Electrical and Computer Engineering
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2974
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Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-3973
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-39732021-08-16T01:35:16Z Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models Magsino, Elmer R. This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral features are derived to highlight the differences between test and training utterances. The CLSU dataset is used to test the efficiency and performance of the proposed CMLLR, Support Vector Machine, and GMM methods for modeling the speaker’s voice by characterizing the speaker features. © 2020, World Academy of Research in Science and Engineering. All rights reserved. 2020-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2974 Faculty Research Work Animo Repository Automatic speech recognition Regression analysis Electrical and Computer Engineering
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
topic Automatic speech recognition
Regression analysis
Electrical and Computer Engineering
spellingShingle Automatic speech recognition
Regression analysis
Electrical and Computer Engineering
Magsino, Elmer R.
Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
description This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral features are derived to highlight the differences between test and training utterances. The CLSU dataset is used to test the efficiency and performance of the proposed CMLLR, Support Vector Machine, and GMM methods for modeling the speaker’s voice by characterizing the speaker features. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
format text
author Magsino, Elmer R.
author_facet Magsino, Elmer R.
author_sort Magsino, Elmer R.
title Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
title_short Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
title_full Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
title_fullStr Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
title_full_unstemmed Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
title_sort speaker feature modeling utilizing constrained maximum likelihood linear regression and gaussian mixture models
publisher Animo Repository
publishDate 2020
url https://animorepository.dlsu.edu.ph/faculty_research/2974
_version_ 1709757396613595136

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