HMM-separation-based speech recognition for a distant moving speaker

This paper presents a hands-free speech recognition method based on HMM composition and separation for speech contaminated not only by additive noise but also by an acoustic transfer function. The method realizes an improved user interface such that a user is not encumbered by microphone equipment in noisy and reverberant environments. The use of HMM composition has already been proposed for countering additive noise. In this paper, the same approach is extended to handle convolutional acoustic distortion in a reverberant room, by using an HMM to model the acoustic transfer function. The states of this HMM correspond to different positions of the sound source. It can represent the positions of the sound sources, even if the speaker moves. This paper also proposes a new method, HMM separation, for estimating the HMM parameters of the acoustic transfer function on the basis of a maximum likelihood manner. The proposed method is obtained through the reverse of the process of HMM composition, where the model parameters are estimated by maximizing the likelihood of adaptation data uttered from an unknown position. Therefore, measurement of impulse responses is not required. The paper also describes the performance of the proposed methods for recognizing real distant-talking speech. The results of experiments clarify the effectiveness of the proposed method.