Speaker-adaptation in a hybrid HMM-MLP recognizer

Presently the most important systems for large vocabulary, continuous speech recognition are speaker-independent. These systems deal with the inter-speaker variability through a large pool of speakers. However, this approach has several drawbacks due to its inability to cope with the individual speaker characteristics. The problem is more extreme for the cases of fast or non-native speakers. In this paper we present a technique for speaker-adaptation in the context of a hybrid HMM-MLP system for large vocabulary, speaker-independent, continuous speech recognition. This technique is implemented both in supervised and unsupervised modes. In the unsupervised case both static and incremental approaches are explored. The results show that speaker-adaptation within the hybrid HMM-MLP framework can substantially improve system performance. In the incremental unsupervised mode, the improvement is obtained without any extra demands on the speaker, i.e. without an enrolment phase.