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 spectral property, and a noisy speech HMM corresponding to each clustered noise and SNR condition is made. Based on the likelihood maximization criterion, an HMM that best matches an input noisy speech is selected and further adapted using linear transformation. The proposed method is evaluated by its ability to recognize noisy broadcast-news speech. It is confirmed that the proposed method is effective in recognizing numerically noise-added speech and actual noisy speech under various noise conditions. The proposed method minimizes mismatches between noisy input speech and the HMMs, sentence by sentence, without requiring online noise spectrum/model estimation. The proposed method is therefore easily applicable to real world conditions with frequently changing noise.
piecewise-linear transformation robust speech recognition noise adaptation noise clustering gmm