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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.