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

Probabilistic linear discriminant analysis (PLDA) acoustic models extend Gaussian mixture models by factorizing the acoustic variability using state-dependent and observation-dependent 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.