| Paper: | SLP-P19.4 | ||
| Session: | Model-based Robust Speech Recognition | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Model-based robust Speech Recognition | ||
| Title: | PATTERN-BASED DYNAMIC COMPENSATION TOWARDS ROBUST SPEECH RECOGNITION IN MOBILE ENVIRONMENTS | ||
| Authors: | Huayun Zhang, Jun Xu, InfoTalk Technology, Singapore | ||
| Abstract: | 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. | ||
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