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Feature-space Speaker Adaptation for Probabilistic Linear Discriminant Analysis Acoustic Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationINTERSPEECH 2015 16th Annual Conference of the International Speech Communication Association
Pages2862-2866
Number of pages5
Publication statusPublished - 11 Sep 2015

Abstract

Probabilistic linear discriminant analysis (PLDA) acoustic models extend Gaussian mixture models by factorizing the acoustic variability using state-dependent and observationdependent variables. This enables the use of higher dimensional
acoustic features, and the capture of intra-frame feature correlations. In this paper, we investigate the estimation of speaker adaptive feature-space (constrained) maximum likelihood linear regression transforms from PLDA-based acoustic models. This feature-space speaker transformation estimation approach is potentially very useful due to the ability of PLDA acoustic models to use different types of acoustic features, for example applying these transforms to deep neural network (DNN) acoustic models for cross adaptation. We evaluated the approach on the Switchboard corpus, and observe significant word error reduction by using both the mel-frequency cepstral coefficients and DNN bottleneck features.

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