LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION
Speaker Identification (SI) aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been pro...
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doaj-3bbb2477917d4d4e9d47492b4e25eab92020-11-24T23:40:59ZengTaylor's UniversityJournal of Engineering Science and Technology1823-46902017-05-0112511531164LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATIONIMEN TRABELSI0MED SALIM BOUHLEL1Sciences and Technologies of image and Telecommunications (SETIT), University of Sfax, TunisiaSciences and Technologies of image and Telecommunications (SETIT), University of Sfax, TunisiaSpeaker Identification (SI) aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM) and Learning vector quantization (LVQ) for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC) extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers) for test data using 36 MFCC features. http://jestec.taylors.edu.my/Vol%2012%20issue%205%20May%202017/12_5_1.pdfSpeaker identificationLVQGMMMFCCTIMIT |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
IMEN TRABELSI MED SALIM BOUHLEL |
spellingShingle |
IMEN TRABELSI MED SALIM BOUHLEL LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION Journal of Engineering Science and Technology Speaker identification LVQ GMM MFCC TIMIT |
author_facet |
IMEN TRABELSI MED SALIM BOUHLEL |
author_sort |
IMEN TRABELSI |
title |
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION |
title_short |
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION |
title_full |
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION |
title_fullStr |
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION |
title_full_unstemmed |
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION |
title_sort |
learning vector quantization for adapted gaussian mixture models in automatic speaker identification |
publisher |
Taylor's University |
series |
Journal of Engineering Science and Technology |
issn |
1823-4690 |
publishDate |
2017-05-01 |
description |
Speaker Identification (SI) aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM)
and Learning vector quantization (LVQ) for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC) extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers) for test data using 36 MFCC features.
|
topic |
Speaker identification LVQ GMM MFCC TIMIT |
url |
http://jestec.taylors.edu.my/Vol%2012%20issue%205%20May%202017/12_5_1.pdf |
work_keys_str_mv |
AT imentrabelsi learningvectorquantizationforadaptedgaussianmixturemodelsinautomaticspeakeridentification AT medsalimbouhlel learningvectorquantizationforadaptedgaussianmixturemodelsinautomaticspeakeridentification |
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1725508436265795584 |