Abstract
We introduce a strategy for modeling speaker variability in speaker adaptation based on maximum likelihood linear regression (MLLR). The approach uses a speaker clustering procedure that models speaker variability by partitioning a large corpus of speakers in the eigenspace of their MLLR transformations and learning cluster specific regression class tree structures. We present experiments showing that choosing the appropriate regression class tree structure for speakers leads to a significant reduction in overall word error rates in automatic speech recognition systems. To realize these gains in unsupervised adaptation, we describe an algorithm that produces a linear combination of MLLR transformations from cluster-specific trees using weights estimated by maximizing the likelihood of a speaker.s adaptation data. […]
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