Abstract
A speech signal captured by a distant microphone is generally smeared by reverberation. This severely degrades both the speech intelligibility and the Automatic Speech Recognition (ASR) performance. We have already proposed a novel dereverberation technique named “Harmonicity based dEReverBeration (HERB),” which utilizes an essential feature of speech, namely harmonics, and estimates an inverse filter for an unknown impulse response. If a large amount of acoustically stable training data is available, HERB is able to estimate an accurate inverse filter even in severely reverberant environments. In general, however, a dereverberation algorithm has to work with small amounts of training data, because the acoustic property of a real world environment changes according to various factors such as the speaker’s position and room temperature. In this paper, we propose a new dereverberation scheme based on HERB, aiming primarily at reducing the amount of training data needed to estimate an inverse filter. The proposed method re-estimates a more accurate source signal based on the output signal of conventional HERB, and re-calculates the 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.
