High-performance HMM adaptation with joint compensation of additive and convolutive distortions via vector Taylor series

Jinyu Li, Li Deng, Dong Yu, Yifan Gong, and Alex Acero

Abstract

In this paper, we present our recent development of a modeldomain

environment-robust adaptation algorithm, which

demonstrates high performance in the standard Aurora 2 speech

recognition task. The algorithm consists of two main steps. First,

the noise and channel parameters are estimated using a nonlinear

environment distortion model in the cepstral domain, the speech

recognizer’s “feedback” information, and the Vector-Taylor-Series

(VTS) linearization technique collectively. Second, the estimated

noise and channel parameters are used to adapt the static and

dynamic portions of the HMM means and variances. This two-step

algorithm enables Joint compensation of both Additive and

Convolutive distortions (JAC).

In the experimental evaluation using the standard Aurora 2

task, the proposed JAC/VTS algorithm achieves 91.11% accuracy

using the clean-trained simple HMM backend as the baseline

system for the model adaptation. This represents high recognition

performance on this task without discriminative training of the

HMM system. Detailed analysis on the experimental results shows

that adaptation of the dynamic portion of the HMM mean and

variance parameters is critical to the success of our algorithm.

Details

Publication typeInproceedings
Published inProc. IEEE Automatic Speech Recognition and Understanding
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