Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

Two-Step Unsupervised Speaker Adaptation Based on Speaker and Gender Recognition and HMM Combination

Petr Cerva, Jan Nouza, Jan Silovsky

Technical University of Liberec, Czech Republic

In this paper, we present a new strategy for unsupervised speaker adaptation. In our approach, the adaptation is performed in two steps for each test utterance. In the first online step, we utilize speaker and gender identification, a set of speaker dependent (SD) hidden Markov models (HMMs) and our own fast linear model combination approach to create a proper model for the first speech recognition pass. After that the recognized phonetic transcription of the utterance is used for maximum likelihood (ML) estimation of more accurate weights for the final model combination step. Our experimental results on different types of broadcast programs show that the proposed method is capable to reduce the word error rate (WER) relatively by more than 17%.

Full Paper

Bibliographic reference.  Cerva, Petr / Nouza, Jan / Silovsky, Jan (2006): "Two-step unsupervised speaker adaptation based on speaker and gender recognition and HMM combination", In INTERSPEECH-2006, paper 1441-Thu1CaP.7.