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ICBA
2004
Springer

Maximum Likelihood and Maximum a Posteriori Adaptation for Distributed Speaker Recognition Systems

9 years 1 months ago
Maximum Likelihood and Maximum a Posteriori Adaptation for Distributed Speaker Recognition Systems
We apply the ETSI’s DSR standard to speaker verification over telephone networks and investigate the effect of extracting spectral features from different stages of the ETSI’s front-end on speaker verification performance. We also evaluate two approaches to creating speaker models, namely maximum likelihood (ML) and maximum a posteriori (MAP), in the context of distributed speaker verification. In the former, random vectors with variances depending on the distance between unquantized training vectors and their closest code vector are added to the vector-quantized feature vectors extracted from client speech. The resulting vectors are then used for creating speaker-dependent GMMs based on ML techniques. For the latter, vector quantized vectors extracted from client speech are used for adapting a universal background model to speaker-dependent GMMs. Experimental results based on 145 speakers from the SPIDRE corpus show that quantized feature vectors extracted from the server sid...
Chin-Hung Sit, Man-Wai Mak, Sun-Yuan Kung
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICBA
Authors Chin-Hung Sit, Man-Wai Mak, Sun-Yuan Kung
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