Automatic speech recognition over error-prone wireless networks

Abstract

The past decade has witnessed a growing interest in deploying automatic speech recognition (ASR) in communication networks. The networks such as wireless networks present a number of challenges due to e.g. bandwidth constraints and transmission errors. The introduction of distributed speech recognition (DSR) largely eliminates the bandwidth limitations and the presence of transmission errors becomes the key robustness issue. This paper reviews the techniques that have been developed for ASR robustness against transmission errors. In the paper, a model of network degradations and robustness techniques is presented. These techniques are classified into three categories: error detection, error recovery and error concealment (EC). A one-frame error detection scheme is described and compared with a frame-pair scheme. As opposed to vector level techniques a technique for error detection and EC at the sub-vector level is presented. A number of error recovery techniques such as forward error correction and interleaving are discussed in addition to a review of both feature-reconstruction and ASR-decoder based EC techniques. To enable the comparison of some of these techniques, evaluation has been conduced on the basis of the same speech database and channel. Special attention is given to the unique characteristics of DSR as compared to streaming audio e.g. voice-over-IP. Additionally, a technique for adapting ASR to the varying quality of networks is presented. The frame-error-rate is here used to adjust the discrimination threshold with the goal of optimising out-of-vocabulary detection. This paper concludes with a discussion of applicability of different techniques based on the channel characteristics and the system requirements.