Efficient Speaker Recognition Using Approximated
Hagai Aronowitz, David Burshtein · 2007
Techniques for efficient speaker recognition are pre- sented. These techniques are based on approximating Gaussian mixture modeling (GMM) likelihood scoring using approximated cross entropy (ACE). Gaussian mixture modeling is used for rep- resenting 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 pre- sented. This algorithm decreases considerably the storage needed for speaker retrieval.