Speaker normalized acoustic modeling based on 3-D Viterbi decoding

This paper describes a novel method for speaker normalization based on a frequency warping approach to reduce variations due to speaker-induced factors such as the vocal tract length. In our approach, a speaker normalized acoustic model is trained using time-varying (i.e., state, phoneme or word dependent) warping factors, while in the conventional approaches, the frequency warping factor is fixed for each speaker. These time-varying frequency warping factors are determined by a 3-dimensional (i.e., input frames, HMM states and warping factors) Viterbi decoding procedure. Experimental results on Japanese spontaneous speech recognition show that the proposed method yields a 9.7% improvement in speech recognition accuracy compared to the conventional speaker-independent model