The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam surat ini, kami mencadangkan pendekatan baharu kepada pengecaman pembesar suara berasaskan SVM, yang menggunakan sejenis maklumat fonotaktik novel sebagai ciri untuk pemodelan SVM. Model campuran Gaussian (GMM) telah terbukti sangat berjaya untuk pengecaman pembesar suara bebas teks. Model latar belakang universal (UBM) GMM ialah model bebas pembesar suara, setiap komponennya boleh dianggap sebagai memodelkan beberapa kelas bunyi fonetik asas. Kami menganggap bahawa ujaran daripada penutur yang berbeza harus mendapat purata kebarangkalian posterior yang berbeza pada komponen Gaussian UBM yang sama, dan supervektor yang terdiri daripada purata kebarangkalian posterior pada semua komponen UBM untuk setiap ujaran harus bersifat diskriminasi. Kami menggunakan supervektor ini sebagai ciri untuk pengecaman pembesar suara berasaskan SVM. Keputusan percubaan pada tugasan NIST SRE 2006 menunjukkan bahawa pendekatan yang dicadangkan menunjukkan prestasi yang setanding dengan sistem yang biasa digunakan. Hasil gabungan juga dibentangkan.
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Salinan
Xiang ZHANG, Hongbin SUO, Qingwei ZHAO, Yonghong YAN, "Using a Kind of Novel Phonotactic Information for SVM Based Speaker Recognition" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 4, pp. 746-749, April 2009, doi: 10.1587/transinf.E92.D.746.
Abstract: In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.746/_p
Salinan
@ARTICLE{e92-d_4_746,
author={Xiang ZHANG, Hongbin SUO, Qingwei ZHAO, Yonghong YAN, },
journal={IEICE TRANSACTIONS on Information},
title={Using a Kind of Novel Phonotactic Information for SVM Based Speaker Recognition},
year={2009},
volume={E92-D},
number={4},
pages={746-749},
abstract={In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.},
keywords={},
doi={10.1587/transinf.E92.D.746},
ISSN={1745-1361},
month={April},}
Salinan
TY - JOUR
TI - Using a Kind of Novel Phonotactic Information for SVM Based Speaker Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 746
EP - 749
AU - Xiang ZHANG
AU - Hongbin SUO
AU - Qingwei ZHAO
AU - Yonghong YAN
PY - 2009
DO - 10.1587/transinf.E92.D.746
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2009
AB - In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.
ER -