CHANNEL-OPTIMIZED ERROR MITIGATION FOR DISTRIBUTED SPEECH RECOGNITION OVER WIRELESS NETWORKS

Abstract

This paper investigates the error mitigation algorithms for distributed speech recognition over wireless channels. A MAP symbol decoding algorithm which exploits the combined a priori information of source and channel is proposed. This is used in conjunction with a modified BCJR algorithm for decoding convolutional codes based on sectionalized code trellises. Performance is further enhanced by the use of the Gilbert channel model that more closely characterizes the statistical dependencies between channel bit errors. Experiments on Mandarin digit string recognition task indicate that our proposed mitigation scheme achieves high robustness against channel errors.