GMM based clustering and speaker separability in the Timit speech database

Speaker recognition on the 630 speaker Timit speech database, using maximum probability selection with a simple Gaussian Mixture Model (GMM) for the data distribution for each speaker, gives above 99% correct recognition. In contrast, a powerful classifier such as a Multi Layer Perceptron (MLP), trained to estimate speaker probabilities, even on a small subset of speakers often performs no better than random selection. We hypothesise two effects which could combine to produce this situation. MLPs do badly because the acoustic feature data is primarily clustered around phonemes, so that speaker classes are highly fragmented and interspersed. In contrast, GMMs model speaker data distributions well because variation within the phonetic cluster identified by each Gaussian is primarily due to speaker variation, with the result that when speaker models are trained by adapting only the means from a multi speaker world model, the resulting GMMs are highly discriminative between speakers. In this article we analyse the distribution of speech and speaker information, both overall and within the cluster identified by each Gaussian in a GMM tuned for speaker recognition on Timit. We show that the results of this analysis support the above hypotheses, and then discuss ways in which the enhanced speaker separability within each Gaussian cluster could be used to harness the discriminative power of MLPs to provide feature data enhancement and improved speaker identification.