Speaker recognition in two-wire test sessions
Hagai Aronowitz, Yosef A. Solewicz
INTERSPEECH 2008
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. © 2006 IEEE.
Hagai Aronowitz, Yosef A. Solewicz
INTERSPEECH 2008
Hagai Aronowitz
ICASSP 2016
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ICASSP 2011
Shai Rozenberg, Hagai Aronowitz, et al.
INTERSPEECH 2020