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