Chapter

Automatic Speech Recognition on Mobile Devices and over Communication Networks

Part of the series Advances in Pattern Recognition pp 163-185

Error Recovery: Channel Coding and Packetization

  • Bengt J. BorgströmAffiliated withDepartment of Electrical Engineering, University of California, Los Angeles
  • , Alexis BernardAffiliated withDepartment of Electrical Engineering, University of California, Los Angeles
  • , Abeer AlwanAffiliated withDepartment of Electrical Engineering, University of California, Los Angeles

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Distributed Speech Recognition (DSR) systems rely on efficient transmission of speech information from distributed clients to a centralized server. Wireless or network communication channels within DSR systems are typically noisy and bursty. Thus, DSR systems must utilize efficient Error Recovery (ER) schemes during transmission of speech information. Some ER strategies, referred to as forward error control (FEC), aim to create redundancy in the source coded bitstream to overcome the effect of channel errors, while others are designed to create spread or delay in the feature stream in order to overcome the effect of bursty channel errors. Furthermore, ER strategies may be designed as a combination of the previously described techniques. This chapter presents an array of error recovery techniques for remote speech recognition applications.

This chapter is organized as follows. First, channel characterization and modeling are discussed. Next, media-specific FEC is presented for packet erasure applications, followed by a discussion on media-independent FEC techniques for bit error applications, including general linear block codes, cyclic codes, and convolutional codes. The application of unequal error protection (UEP) strategies utilizing combinations of the aforementioned FEC methods is also presented. Finally, frame-based interleaving is discussed as an alternative to overcoming the effect of bursty channel erasures. The chapter concludes with examples of modern standards for channel coding strategies for distributed speech recognition (DSR).