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A new method for speaker adaptation using bilinear model

In this paper, a novel method for speaker adaptation using bilinear model is proposed. Bilinear model can express both characteristics of speakers (style) and phonemes across speakers (content) independently in a training database. The mapping from each speaker and phoneme space to observation space is carried out using bilinear mapping matrix which is independent of speaker and phoneme space. We apply the bilinear model to speaker adaption. Using adaptation data from a new speaker, speaker-adapted model is built by estimating the style(speaker)-specific matrix. Experimental results showed that the proposed method outperformed eigenvoice and MLLR. In vocabulary-independent isolated word recognition for speaker adaptation, bilinear model reduced word error rate by about 38% and about 10% compared to eigenvoice and MLLR respectively using 50 words for adaptation.