Alexis Bernard

Low-bitrate distributed speech recognition for packet-based and wireless communication

We present a framework for developing source coding, channel coding and decoding as well as erasure concealment techniques adapted for distributed (wireless or packet-based) 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 the 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 bit rates of 1.2 kbps or less.

An embedded adaptive multi-rate wideband speech coder

This paper presents a multi-rate wideband speech coder with bit rates from 8 to 32 kb/s. The coder uses a splitband approach, where the input signal, sampled at 16 kHz, is split into two equal frequency bands from 0-4 kHz and 4-8 kHz, each of which is decimated to an 8 kHz sampling rate. The lower band is coded using the adaptive multi-rate (AMR) family of high-quality narrowband speech coders, while the higher band is represented by a simple but effective parametric model. A complete solution including this wideband speech coder, channel coding for various GSM channels, and dynamic rate adaptation, easily passed all Selection Rules and ranked second overall in the 3GPP AMR Wideband Selection Testing. Besides the high performance, additional advantages of the embedded split-band approach include ease of implementation, reduced complexity, and simplified interoperation with narrowband speech coders.

Speech transmission using rate-compatible trellis codes and embedded source coding

This paper presents bandwidth-efficient speech transmission systems using rate-compatible channel coders and variable bitrate embedded source coders. Rate-compatible punctured convolutional codes (RCPC) are often used to provide unequal error protection (UEP) via progressive bit puncturing. RCPC codes are well suited for constellations for which Euclidean and Hamming distances are equivalent (BPSK and 4-PSK). This paper introduces rate-compatible punctured trellis codes (RCPT) where rate compatibility and UEP are provided via progressive puncturing of symbols in a trellis. RCPT codes constitute a special class of codes designed to maximize residual Euclidean distances (RED) after symbol puncturing. They can be designed for any constellation, allowing for higher throughput than when restricted to using 4-PSK. We apply RCPC and RCPT to two embedded source coders: a perceptual subband coder and the ITU embedded ADPCM G.727 standard. Different operating modes with distinct source/channel bit allocation and UEP are defined. Each mode is optimal for a certain range of AWGN channel SNRs. Performance results using an 8-PSK constellation clearly illustrate the wide range of channel conditions at which the adaptive scheme using RCPT can operate. For an 8-PSK constellation, RCPT codes are compared to RCPC with bit interleaved coded modulation codes (RCPC-BICM). We also compare performance to RCPC codes used with a 4-PSK constellation.

Source and channel coding for remote speech recognition over error-prone channels

This paper presents source and channel coding techniques for remote automatic speech recognition (ASR) systems. As a case study, line spectral pairs (LSP) extracted from the 6th order all-pole perceptual linear prediction (PLP) spectrum are transmitted and speech recognition features are then obtained. The LSPs, quantized using first-order predictive vector quantization (VQ) at 300 bps, provide recognition accuracy comparable to that of the baseline system with no quantization. A new soft decision channel decoding scheme appropriate for remote recognition is presented. The scheme outperforms commonly-used hard decision decoding in terms of error correction and error detection. The source and channel coding system operates at 500 bps and provides good digit recognition performance over a wide range of channel conditions.

Embedded joint source-channel coding of speech using symbol puncturing of trellis codes

This paper presents an embedded joint source-channel coding scheme of speech. The source coder is an embedded variable bit rate perceptually based sub-band coder producing bits with different error sensitivities. The channel encoder is a rate compatible punctured trellis code (RCPT) which permits rate variability and unequal error protection by puncturing symbols. Furthermore, RCPT code design naturally incorporates large constellations, allowing high information rate per symbol. The embedded speech coder and the rate compatible puncturing of symbols provide the embeddibility of the joint coding scheme. The coder is robust to acoustic noise and produces good quality speech for a wide range of channel conditions (AWGN or fading), allowing digital transmission of speech with analog-like graceful degradation.

Channel adaptive joint source-channel coding of speech

We present a novel joint source-channel coding scheme for speech signals. The source coder is a perceptually based sub-band coder producing bits with different error sensitivities. The channel encoder is a rate compatible punctured trellis code (RCPT) that permits unequal error protection. The RCPT code design naturally incorporates large constellations, allowing a high information rate per symbol. The source coder is robust to acoustic noise, adapts automatically every 20 ms and produces good quality speech for a wide range of channel conditions. The paper presents a method for finding the optimal puncturing architecture for different source bit rates and channel conditions. The resulting joint source-channel coder is suitable for situations where the channel is time varying such as in mobile communications.

Error Recovery: Channel Coding and Packetization

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, framebased 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). 8.1. Distributed Speech Recognition Systems Throughout this chapter various error recovery and detection techniques are discussed. It is therefore necessary to present an overview of a complete experimental DSR system, including feature extraction, a noisy channel model, and an automatic speech recognition engine at the server end (Fig. 8.1). 2 Bengt J. Borgstrom, Alexis Bernard, and Abeer Alwan Feature Extraction Source Coding Channel Coding Channel Decoding Source Decoding Speech Recognition Client Server Noisy Channel input speech