Piecewise-linear transformation-based HMM adaptation for noisy speech

Abstract

This paper proposes a new method using a piecewise-linear transformation for adapting phone HMM to noisy speech. Various noises are clustered according to their acoustic properties and signal-to-noise ratios (SNR), and a noisy speech HMM corresponding to each clustered noise is made. Based on the likelihood maximization criterion, the HMM which best matches the input speech is selected and further adapted using a 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.