In this paper, we present a framework for developing source coding, channel coding and decoding as well as erasure concealment techniques adapted for distributed (wireless or packetbased) speech recognition. It is shown that speech recognition as opposed to speech coding, is more sensitive to channel errors than channel erasures, and appropriate channel coding design criteria are determined. For channel decoding, we introduce a novel technique for combining at the receiver soft decision decoding with error detection. Frame erasure concealment techniques are used at the decoder to deal with unreliable frames. At the recognition stage, we present a technique to modify the recognition engine itself to take into account the time-varying reliability of the decoded feature after channel transmission. The resulting engine, referred to as weighted Viterbi recognition, further improves recognition accuracy. Together, source coding, channel coding and the modified recognition engine are shown to provide good recognition accuracy over a wide range of communication channels with bitrates of 1.2 kbps or less.

The Viterbi algorithm has been successfully applied to different pattern recognition and communi-cation tasks. However, if some observations are corrupted by unknown impulsives noise which are not accounted for by the distortion measures, recognition performance can degrade significantly. In this paper, we propose a robust Viterbi algorithm to handle short, impulsive noises with unknown characteristics by means of joint decoding and detection during the Viterbi search. To make the algorithm applicable to different noisy conditions with varying amounts of impulsive noise, we further proposed an approach to efficiently estimate the number of corruptions. We demonstrate the effectiveness of the proposed robust algorithms using spoken digit recognition experiments under two different impulsive noise environments. Under random Gaussian replacement noise, the proposed algorithm reduced digit error by more than 65%. Under the GSM network environment in which lost frames are replaced by interpolated neighboring frames, the robust algorithm reduced digit error by 20%. Furthermore, the proposed algorithm does not degrade performance when impulsive noise is not present.

Conventional circuit-switched networks are increasingly being replaced by packet-based networks for voice communication applications. Additionally, there has been an increased deployment of services supporting speech based interactions. These trends demand reliable transmission of speech data not just for playback but also to ensure acceptable automatic speech recognition (ASR) performance. In this paper, we present an overview of techniques that have been investigated to improve ASR performance against two major degradation factors in the context of packet networks: (1) information loss due to a low bit-rate codec and (2) packet loss due to channel (network) conditions. In addition, we highlight another key issue, packet loss rate, by showing ASR performance as a function of packet size and channel condition. 1.

In this study we evaluate transmission error compensation techniques for distributed speech recognition systems based on modification of the speech decoder. The candidates are marginalization, weighted Viterbi and our recently proposed soft-feature uncertainty decoding. For the latter, it is shown how the Bayesian speech recognition approach must be reformulated for recognition at the server side. The resulting predictive classifier is able to take account of the transmission errors by changing the contribution of the affected speech features to the acoustic score. The comparison of the experimental results has proven the superiority of our approach. 1.