Covariance estimation methods for channel robust text-independent speaker identification

Two novel channel robust methods are described for performing text-independent speaker identification. The first technique models speaker´s voices stochastically via cepstra correlations rather than by covariances in an effort to compensate for additive noise. The second technique, which we term dynamic covariances, models speakers by covariances of deviations of cepstra from time varying means rather than from constant means. Dynamic covariances may normalize for time varying channel effects, utterance lengths and text. Experimental results are obtained on the SPIDRE subset of the Switchboard corpus. Error rates as low as 2.2% are obtained using the new models