Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition

Chang Huai You; Haizhou Li; Bin Ma; Kong Aik Lee

Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition

Chang Huai You
, Haizhou Li
, Bin Ma
, Kong Aik Lee

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

Gaussian mixture model - support vector machine (GMMSVM) with nuisance attribute projection (NAP) has been found to be effective and reliable for speaker and language recognition. In maximum a posteriori (MAP) adaptation of GMM, the relevance factor is the parameter that regulates how much the adaptation data affect the base model, which impacts the final recognition performance. In our previous work, the data-dependent relevance factor and adaptive relevance factor have been introduced. In this paper, we provide insights into different types of relevance factor for MAP in the context of application as formulated under Speaker Recognition Evaluation (SRE) and Language Recognition Evaluation (LRE) by the National Institute of Standards and Technology (NIST).

Original language	English
Title of host publication	13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Pages	2063-2066
Number of pages	4
Publication status	Published - Sept 2012
Externally published	Yes
Event	13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 - Portland, OR, United States Duration: 9 Sept 2012 → 13 Sept 2012

Publication series

Name	13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Volume	3

Conference

Conference	13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Country/Territory	United States
City	Portland, OR
Period	9/09/12 → 13/09/12

Keywords

Gaussian mixture model
Maximum a posteriori
Supervector
Support vector machine

ASJC Scopus subject areas

Computer Networks and Communications
Communication

Cite this

You, C. H., Li, H., Ma, B., & Lee, K. A. (2012). Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition. In 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 (pp. 2063-2066). (13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012; Vol. 3).

You, Chang Huai ; Li, Haizhou ; Ma, Bin et al. / Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition. 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012. 2012. pp. 2063-2066 (13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012).

@inproceedings{912334fc2bd14ee9b07fd143dc703014,

title = "Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition",

abstract = "Gaussian mixture model - support vector machine (GMMSVM) with nuisance attribute projection (NAP) has been found to be effective and reliable for speaker and language recognition. In maximum a posteriori (MAP) adaptation of GMM, the relevance factor is the parameter that regulates how much the adaptation data affect the base model, which impacts the final recognition performance. In our previous work, the data-dependent relevance factor and adaptive relevance factor have been introduced. In this paper, we provide insights into different types of relevance factor for MAP in the context of application as formulated under Speaker Recognition Evaluation (SRE) and Language Recognition Evaluation (LRE) by the National Institute of Standards and Technology (NIST).",

keywords = "Gaussian mixture model, Maximum a posteriori, Supervector, Support vector machine",

author = "You, \{Chang Huai\} and Haizhou Li and Bin Ma and Lee, \{Kong Aik\}",

year = "2012",

month = sep,

language = "English",

isbn = "9781622767595",

series = "13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012",

pages = "2063--2066",

booktitle = "13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012",

note = "13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 ; Conference date: 09-09-2012 Through 13-09-2012",

}

You, CH, Li, H, Ma, B & Lee, KA 2012, Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition. in 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012. 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, vol. 3, pp. 2063-2066, 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, Portland, OR, United States, 9/09/12.

Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition. / You, Chang Huai; Li, Haizhou; Ma, Bin et al.
13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012. 2012. p. 2063-2066 (13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012; Vol. 3).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition

AU - You, Chang Huai

AU - Li, Haizhou

AU - Ma, Bin

AU - Lee, Kong Aik

PY - 2012/9

Y1 - 2012/9

N2 - Gaussian mixture model - support vector machine (GMMSVM) with nuisance attribute projection (NAP) has been found to be effective and reliable for speaker and language recognition. In maximum a posteriori (MAP) adaptation of GMM, the relevance factor is the parameter that regulates how much the adaptation data affect the base model, which impacts the final recognition performance. In our previous work, the data-dependent relevance factor and adaptive relevance factor have been introduced. In this paper, we provide insights into different types of relevance factor for MAP in the context of application as formulated under Speaker Recognition Evaluation (SRE) and Language Recognition Evaluation (LRE) by the National Institute of Standards and Technology (NIST).

AB - Gaussian mixture model - support vector machine (GMMSVM) with nuisance attribute projection (NAP) has been found to be effective and reliable for speaker and language recognition. In maximum a posteriori (MAP) adaptation of GMM, the relevance factor is the parameter that regulates how much the adaptation data affect the base model, which impacts the final recognition performance. In our previous work, the data-dependent relevance factor and adaptive relevance factor have been introduced. In this paper, we provide insights into different types of relevance factor for MAP in the context of application as formulated under Speaker Recognition Evaluation (SRE) and Language Recognition Evaluation (LRE) by the National Institute of Standards and Technology (NIST).

KW - Gaussian mixture model

KW - Maximum a posteriori

KW - Supervector

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/84878593987

M3 - Conference article published in proceeding or book

AN - SCOPUS:84878593987

SN - 9781622767595

T3 - 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012

SP - 2063

EP - 2066

BT - 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012

T2 - 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012

Y2 - 9 September 2012 through 13 September 2012

ER -

Effect of relevance factor of maximum a posteriori adaptation for GMM-SVM in Speaker and Language Recognition

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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