Model-based blind estimation of reverberation time: application to robust ASR in reverberant environments

Abstract

This paper presents a method for blind estimation of reverberation times in reverberant enclosures. The proposed algorithm is based on a statistical model of short-term log-energy sequences for echo-free speech. Given a speech utterance recorded in a reverberant room, it computes a Maximum Likelihood estimate of the room full-band reverberation time. The estimation method is shown to require little data and to perform satisfactorily. The method has been successfully applied to robust automatic speech recognition in reverberant environments by model selection. For this application, the reverberation time is first estimated from the reverberated speech utterance to be recognized. The estimation is then used to select the best acoustic model out of a library of models trained in various artificial reverberant conditions. Speech recognition experiments in simulated and real reverberant environments show the efficiency of our approach which outperforms standard channel normalization techniques.