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EXTENDED VTS FOR NOISE-ROBUST SPEECH RECOGNITION
| Citations: | 14 - 10 self |
BibTeX
@MISC{Dalen_extendedvts,
author = {R. C. Van Dalen and M. J. F. Gales},
title = {EXTENDED VTS FOR NOISE-ROBUST SPEECH RECOGNITION},
year = {}
}
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Abstract
Model compensation is a standard way of improving speech recognisers’ robustness to noise. Currently popular schemes are based on vector Taylor series (VTS) compensation. They often use the continuous time approximation to compensate dynamic parameters. In this paper, the accuracy of dynamic parameter compensation is improved by representing the dynamic features as a linear transformation of a window of static features. A modified version of VTS compensation is applied to the distribution of the window of static features and, importantly, their correlations. These compensated distributions are then transformed to standard static and dynamic distributions. The proposed scheme outperformed the standard VTS scheme by about 10 % relative. Index Terms — Speech recognition, acoustic noise, robustness 1.
Keyphrases
extended vt noise-robust speech recognition static feature standard way acoustic noise linear transformation proposed scheme currently popular scheme vt compensation standard vt scheme dynamic parameter continuous time approximation dynamic feature modified version dynamic parameter compensation index term speech recognition vector taylor series speech recognisers dynamic distribution model compensation compensated distribution
