A tree-structured clustering method integrating noise and SNR for piecewise linear-transformation-based noise adaptation

This paper proposes the application of a tree-structured clustering method that integrates the effects of noise as well as SNR variation in the framework of piecewise-linear transformation (PLT)-based noise adaptation for robust speech recognition. According to the clustering results, a noisy speech HMM is made for each node of the tree structure. An HMM that best matches the input speech is selected based on the likelihood maximization criterion by tracing the tree downward from the top (root), and the selected HMM is further adapted by linear transformation. The proposed method is evaluated by applying it to a Japanese dialogue recognition system. Experimental results confirm that the proposed method is effective in recognizing numerically noise-added speech and actual noisy speech uttered by a wide range of speakers under various noise conditions.