Using maximum likelihood linear regression for segment clustering and speaker identification
Michiel Bacchiani · 2000
Many adaptation scenarios rely on clustering of either the test or training data. Although consistency between the clustering and adaptation objective functions is desired, most previous approaches have not implemented such consistency. This paper shows that the statistics used in Maximum Likelihood Linear Regression (MLLR) adaptation are sufficient to cluster data with a consistent Maximum Likelihood (ML) criterion. In addition, as the algorithm uses the same statistics for both adaptation and clustering, it is computationally efficient. Clustering experiments contrasting the performance of this algorithm with the widely used text independent Gaussian mixture model approach show increased adaptation likelihoods and consistency of within-cluster speaker identity. In a speaker identification experiment the adaptation-based scoring showed improved classification performance compared to the mixture model-based scoring.