5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

GMM Sample Statistic Log-Likelihoods for Text-Independent Speaker Recognition

Michael Schmidt, John Golden, Herbert Gish

BBN Systems and Technologies, Cambridge, MA, USA

A novel approach to scoring Gaussian mixture mod- els is presented. Feature vectors are assigned to the individual Gaussians making up the model and log-likelihoods of the separate Gaussians are computed and summed. Furthermore, the log-likelihoods of the individual Gaussians can be decomposed into sample weight, mean, and covariance log-likelihoods. Correlation likelihoods can also be computed. The results of the various systems are comparable on text- independent speaker recognition experiments despite the fact that the models and scoring are all quite di erent. By decomposing log-likelihoods of models into various sample statistic log-likelihoods, it is possible to diagnose which part of the model has the greatest discriminative power, whether the location of the Gaussians or their shapes.

Full Paper

Bibliographic reference.  Schmidt, Michael / Golden, John / Gish, Herbert (1997): "GMM sample statistic log-likelihoods for text-independent speaker recognition", In EUROSPEECH-1997, 855-858.