Piecewise-linear transformation-based HMM adaptation for noisy speech

This paper proposes a new method using piecewise-linear transformation for adapting phone HMMs to noisy speech. Various noises are clustered according to their acoustical property and signal-to-noise ratios (SNRs), and noisy speech HMM corresponding to each clustered noise is made. Based on the likelihood maximization criterion, the HMM which best matches an input speech is selected and further adapted using linear transformation. The proposed method was evaluated by recognizing noisy broadcast-news speech. It was confirmed that the proposed method was effective in recognizing numerically noise-added speech and actual noisy speech by a wide range of speakers under various noise conditions.