Efficient Speaker Recognition Using Approximated Cross Entropy (ACE)

Techniques for efficient speaker recognition are presented. These techniques are based on approximating Gaussian mixture modeling (GMM) likelihood scoring using approximated cross entropy (ACE). Gaussian mixture modeling is used for representing both training and test sessions and is shown to perfor...

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Bibliographic Details
Published inIEEE transactions on audio, speech, and language processing Vol. 15; no. 7; pp. 2033 - 2043
Main Authors Aronowitz, H., Burshtein, D.
Format Journal Article
LanguageEnglish
Published IEEE 01.09.2007
Subjects
Acoustic testing
Algorithms
Approximation
Compressing
Compression algorithms
Degradation
Entropy
Gaussian
Indexing
Loudspeakers
Parametric statistics
Performance evaluation
Retrieval
Scoring
Speaker identification
speaker indexing
Speaker recognition
speaker retrieval
speaker verification
Speech recognition
System testing
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Summary:Techniques for efficient speaker recognition are presented. These techniques are based on approximating Gaussian mixture modeling (GMM) likelihood scoring using approximated cross entropy (ACE). Gaussian mixture modeling is used for representing both training and test sessions and is shown to perform speaker recognition and retrieval extremely efficiently without any notable degradation in accuracy compared to classic GMM-based recognition. In addition, a GMM compression algorithm is presented. This algorithm decreases considerably the storage needed for speaker retrieval.
Bibliography:ObjectType-Article-2
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ISSN:1558-7916
1558-7924
DOI:10.1109/TASL.2007.902059