In this paper, we present a new approach to HMM adaptation

that jointly compensates for additive and convolutive acoustic

distortion in environment-robust speech recognition. The hallmark

of our new approach is the use of a nonlinear, phase-sensitive

model of acoustic distortion that captures phase asynchrony

between clean speech and the mixing noise. In the first step of the

developed algorithm, both the static and dynamic portions of the

noise and channel parameters are estimated in the cepstral domain,

using the speech recognizer’s “feedback” information and the

vector-Taylor-series linearization technique on the nonlinear

phase-sensitive model. In the second step, the estimated noise and

channel parameters are used to effectively adapt the static and

dynamic portions of the HMM means and variances also using the

linearized phase-sensitive acoustic distortion model.

In the experimental evaluation using the standard Aurora 2

task, the proposed new algorithm achieves 93.3% accuracy using

the clean-trained complex HMM backend as the baseline system

for unsupervised HMM adaptation. This reaches the highest

performance number in the literature on this task with cleantrained

HMM model. The experimental results show that the phase

term, which was missing in all previous HMM-adaptation work,

contributes significantly to the achieved high recognition accuracy.

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In  Proc. ICASSP

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