GMM based speaker identification using training-time-dependent number of mixtures

In this paper, we present the study of the performance of our standard Gaussian mixture model (GMM) speaker identification system in “a limited amount of training data” context. We explore the use of different mixture components for different speakers/models. Different approaches are presented: (a) A nonlinear transformation of speech duration vs. number of mixtures is proposed in order to set correctly the appropriate number of model mixtures for each speaker according to the available training data. (b) From exhaustive experiments, the appropriate linear transformation is deduced. The resulting transformation offers several advantages: (a) each speaker is well modelized, (b) the performance is improved by more than 6% on the SPIDRE corpus and finally (c) the number of mixtures is reduced and thus leads to a faster system response