A corpus-based approach for robust ASR in reverberant environments
Laurent Couvreur, Christophe Couvreur, Christophe Ris · 2000
In this paper, we discuss the use of artificial room reverberation to increase the performance of automatic speech recognition (ASR) systems in reverberant enclosures. Our approach consists in training acoustic models on artificially reverberated speech material. In order to obtain the desired reverberated speech training database, we propose to use a reverberating filter whose impulse response is designed to match two high-level acoustic properties of the target reverberant operating environment, namely the earlyto-late energy ratio and the reverberation time. Speech recognition experiments in simulated reverberant environments show that recognizers trained on speech reverberated with the proposed method outperform systems trained on clean speech, even when channel normalization methods like CMS and logRASTA-PLP are used. The extension of our approach to multi-style training is also considered. 1.