Combining GMM's with support vector machines for text-independent speaker verification

Jamal Kharroubi; Dijana Petrovska-Delacrétaz; Gérard Chollet

Combining GMM's with support vector machines for text-independent speaker verification

Jamal Kharroubi
, Dijana Petrovska-Delacrétaz
, Gérard Chollet

CNRS LTCI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

Current best performing speaker recognition algorithms are based on Gaussian Mixture Models (GMM). Their results are not satisfactory for all experimental conditions, especially for the mismatched (train/test) conditions. Support Vector Machine is a new and very promissing technique in statistical learning theory. Recently, this technique produced very interesting results in image processing [2], [3], [4] and for the fusion of experts in biometric authentification [5]. In this paper we address the issue of using the Support Vector Learning technique in combination with the currently well performing GMM models, in order to improve speaker verification results.

Original language	English
Title of host publication	EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology
Editors	Borge Lindberg, Henrik Benner, Paul Dalsgaard, Zheng-Hua Tan
Publisher	International Speech Communication Association
Pages	1761-1764
Number of pages	4
ISBN (Electronic)	8790834100, 9788790834104
Publication status	Published - 1 Jan 2001
Externally published	Yes
Event	7th European Conference on Speech Communication and Technology - Scandinavia, EUROSPEECH 2001 - Aalborg, Denmark Duration: 3 Sept 2001 → 7 Sept 2001

Publication series

Name	EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology

Conference

Conference	7th European Conference on Speech Communication and Technology - Scandinavia, EUROSPEECH 2001
Country/Territory	Denmark
City	Aalborg
Period	3/09/01 → 7/09/01

Cite this

Kharroubi, J., Petrovska-Delacrétaz, D., & Chollet, G. (2001). Combining GMM's with support vector machines for text-independent speaker verification. In B. Lindberg, H. Benner, P. Dalsgaard, & Z.-H. Tan (Eds.), EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology (pp. 1761-1764). (EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology). International Speech Communication Association.

Kharroubi, Jamal ; Petrovska-Delacrétaz, Dijana ; Chollet, Gérard. / Combining GMM's with support vector machines for text-independent speaker verification. EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology. editor / Borge Lindberg ; Henrik Benner ; Paul Dalsgaard ; Zheng-Hua Tan. International Speech Communication Association, 2001. pp. 1761-1764 (EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology).

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title = "Combining GMM's with support vector machines for text-independent speaker verification",

abstract = "Current best performing speaker recognition algorithms are based on Gaussian Mixture Models (GMM). Their results are not satisfactory for all experimental conditions, especially for the mismatched (train/test) conditions. Support Vector Machine is a new and very promissing technique in statistical learning theory. Recently, this technique produced very interesting results in image processing [2], [3], [4] and for the fusion of experts in biometric authentification [5]. In this paper we address the issue of using the Support Vector Learning technique in combination with the currently well performing GMM models, in order to improve speaker verification results.",

author = "Jamal Kharroubi and Dijana Petrovska-Delacr{\'e}taz and G{\'e}rard Chollet",

year = "2001",

month = jan,

day = "1",

language = "English",

series = "EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology",

publisher = "International Speech Communication Association",

pages = "1761--1764",

editor = "Borge Lindberg and Henrik Benner and Paul Dalsgaard and Zheng-Hua Tan",

booktitle = "EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology",

note = "7th European Conference on Speech Communication and Technology - Scandinavia, EUROSPEECH 2001 ; Conference date: 03-09-2001 Through 07-09-2001",

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Kharroubi, J, Petrovska-Delacrétaz, D & Chollet, G 2001, Combining GMM's with support vector machines for text-independent speaker verification. in B Lindberg, H Benner, P Dalsgaard & Z-H Tan (eds), EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology. EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology, International Speech Communication Association, pp. 1761-1764, 7th European Conference on Speech Communication and Technology - Scandinavia, EUROSPEECH 2001, Aalborg, Denmark, 3/09/01.

Combining GMM's with support vector machines for text-independent speaker verification. / Kharroubi, Jamal; Petrovska-Delacrétaz, Dijana; Chollet, Gérard.
EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology. ed. / Borge Lindberg; Henrik Benner; Paul Dalsgaard; Zheng-Hua Tan. International Speech Communication Association, 2001. p. 1761-1764 (EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combining GMM's with support vector machines for text-independent speaker verification

AU - Kharroubi, Jamal

AU - Petrovska-Delacrétaz, Dijana

AU - Chollet, Gérard

PY - 2001/1/1

Y1 - 2001/1/1

N2 - Current best performing speaker recognition algorithms are based on Gaussian Mixture Models (GMM). Their results are not satisfactory for all experimental conditions, especially for the mismatched (train/test) conditions. Support Vector Machine is a new and very promissing technique in statistical learning theory. Recently, this technique produced very interesting results in image processing [2], [3], [4] and for the fusion of experts in biometric authentification [5]. In this paper we address the issue of using the Support Vector Learning technique in combination with the currently well performing GMM models, in order to improve speaker verification results.

AB - Current best performing speaker recognition algorithms are based on Gaussian Mixture Models (GMM). Their results are not satisfactory for all experimental conditions, especially for the mismatched (train/test) conditions. Support Vector Machine is a new and very promissing technique in statistical learning theory. Recently, this technique produced very interesting results in image processing [2], [3], [4] and for the fusion of experts in biometric authentification [5]. In this paper we address the issue of using the Support Vector Learning technique in combination with the currently well performing GMM models, in order to improve speaker verification results.

M3 - Conference contribution

AN - SCOPUS:85009112344

T3 - EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology

SP - 1761

EP - 1764

BT - EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology

A2 - Lindberg, Borge

A2 - Benner, Henrik

A2 - Dalsgaard, Paul

A2 - Tan, Zheng-Hua

PB - International Speech Communication Association

T2 - 7th European Conference on Speech Communication and Technology - Scandinavia, EUROSPEECH 2001

Y2 - 3 September 2001 through 7 September 2001

ER -

Kharroubi J, Petrovska-Delacrétaz D, Chollet G. Combining GMM's with support vector machines for text-independent speaker verification. In Lindberg B, Benner H, Dalsgaard P, Tan ZH, editors, EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology. International Speech Communication Association. 2001. p. 1761-1764. (EUROSPEECH 2001 - SCANDINAVIA - 7th European Conference on Speech Communication and Technology).

Combining GMM's with support vector machines for text-independent speaker verification

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