Anisotropic MAP defined by eigenvoices for large vocabulary continuous speech recognition

A general method is examined, which unifies the eigenvoice approach [1, 2, 4] and MAP adaptation. The a priori distribution for MAP is chosen to be anisotropic with the eigenvoices as preferred directions while still allowing adaptation into all other directions. This allows the exploitation of a priori knowledge about typical speaker variability within the MAP framework. This approach has two advantages: long term adaptation leads to the same good results as the MAP method, whereas for ultra-short adaptation in the range of 1–2 seconds an overfitting as for maximum likelihood techniques is avoided. The method is applied to large vocabulary continuous speech recognition. Results are to be compared with our recent transfer of the maximum likelihood eigenvoice method to LVCSR [4]. Even after only one recognized word significant improvements of the WER of up to 6% relative are observed for gender independent recognition. 14% improvement are obtained after seconds.