Fast Estimation of a Precise Dereverberation Filter based on Speech Harmonicity

A speech signal captured by a distant microphone is generally smeared by reverberation. This severely degrades both the speech intelligibility and automatic speech recognition (ASR) performance. In this paper, we propose a new dereverberation scheme based on harmonicity based dereverberation (HERB), aiming primarily at reducing the amount of training data needed to estimate an inverse filter. We show experimentally that our new dereverberation scheme successfully achieves high quality dereverberation with much smaller amounts of training data, and is very effective at improving both audible quality and ASR performance, even in unknown severely reverberant environments.