Extraction of Reliable Transformation Parameters for Unsupervised Speaer Adaptation

Adaptation of speaker-independent hidden Markov models (HMM’s) to a new speaker using speaker-specific data is an effective approach to reinforce speech recognition performance for the enrolled speaker. Practically, it is desirable to flexibly perform the adaptation without any knowledge or limitation on the enrolled adaptation data (e.g. data transcription, length and content). However, the inevitable transcription errors on adaptation data may cause unreliability in model adaptation. The variable amount and content of adaptation data require the algorithm to dynamically control the degrees of sharing in transformation-based adaptation. This paper presents an unsupervised hierarchical adaptation algorithm where a tree structure of HMM’s is incorporated to control the transformation sharing. To extract reliable transformation parameters, we exploit the reliability assessment criteria using the confidence measure and description length. Experiments show that the unsupervised speaker adaptation with reliability assessment can significantly improve the recognition performance for any lengths of adaptation data.