| | |
Summary: SOURCE AND CHANNEL CODING FOR REMOTE SPEECH RECOGNITION OVER
ERROR-PRONE CHANNELS
Alexis Bernard and Abeer Alwan
Dept. of Electrical Engineering, UCLA
Los Angeles, CA 90095
fabernard, alwang@icsl.ucla.edu
ABSTRACT
This paper presents source and channel coding techniques for re-
mote 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 transmit-
ted 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.
|
