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Pattern-Based Dynamic Compensation Towards Robust Speech Recognition in Mobile Environments

Today, the high mobility provided by wireless networks places users in a wild variety of noise and channel conditions, which poses serious challenge to telephone-base acoustic speech recognition (ASR). In this paper, we propose a pattern-based dynamic compensation (PDC) scheme to improve the robustness of ASR in mobile environments. In PDC, a distortion pattern-set is employed to normalize the environmental variations in training data according to a set of pre-defined application scenarios. At recognition time, instantaneous distortion is calculated as a linear combination of several possible patterns. To online estimate the combination weights robustly, a Bayesian learning process with speech-conditioned prior evolution is introduced into PDC (PDC-SPE). In outdoor experiments, the PDC-SPE method outperforms other commonly used compensation/adaptation methods and leads to 20-25% relative reduction in word error rate (WER) over a well-trained baseline system