Low Bit-Rate Encoding Algorithm for Distributed Speech Recognition Based on SVD Decomposition
Keywords:
Distributed speech recognition, vector and scalar quantizers, singular value decomposition, aurora-2 database.Abstract
The paper presents an algorithm for compression
of front-end feature extracted parameters used in Distributed
Speech Recognition (DSR). In the proposed method the source
encoder is mainly based on truncated Singular Value
Decomposition transform (SVD) with conventional vector and
scalar quantizers. The system provides a compression bit-rate
around 3500 bps. The experiments were carried out on the
TIDigitsAurora-2 database using Hidden Makcov Model
Toolkit (HTK). The simulation results show good recognition
performance without
dramatic change,
comparing
toconventional ETSI Aurorastandard front-end feature
compression algorithm with quantized features at 4400 bps.
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