Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
On stochastic feature and model compensation approaches to robust speech recognition
Speech Communication - Special issue on robust speech recognition
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
A speaker tracking system based on speaker turn detection for NIST evaluation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
IEEE Transactions on Signal Processing
Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR
International Journal of Speech Technology
| Hi-index | 0.00 |
This paper treats the problem of noise compensation in speech recognition when training and testing conditions do not match. We are interested in two types of non-stationary noise that may be present during test, namely slowly varying and abruptly varying noises. The context of our work is the Stochastic Matching framework. The Stochastic Matching compensation method transforms test data using an affine compensation function whose parameters are computed off-line. Stochastic Matching approaches are interesting since they make little assumptions about the nature and the level of the noise but they are best suited for the compensation of stationary noise. In this paper we propose an original contribution to the Stochastic Matching framework. It is based on an on-line frame-synchronous version of Stochastic Matching method to compensate for slowly varying noise. Our contribution extends this compensation algorithm in order to compensate for abruptly varying noise. The basic idea of the proposed methods is to perform the compensation and the recognition steps at the same time. The environment changes are identified using monitoring algorithms. The performance of our proposed methods is evaluated on two speech databases, one recorded in moving cars (VODIS), and another one obtained by corrupting VODIS with abruptly varying noise from NOISEX. The proposed approaches significantly outperform classical compensation methods (Off-line Stochastic Matching, Sequential Mean Cepstrum Removal, Parallel Model Combination, Spectral Subtraction). For instance, we obtain up to 32.6% word error rate reduction over S-MCR on database corrupted by a 10dB abruptly varying white noise.