Speech recognition for a distant moving speaker based on HMM composition and separation

This paper describes 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 re­ alizes an improved user interface such that a user is not encumbered by microphone equipment in noisy and re­ verberant environments. In this approach， an attempt is made to model acoustic transfer functions by meωlS of an ergodic H:rvlM [1]. The states of this HM:tvl cor­ respond to different positions of the sound source. It can represent the positions of the sound sources， even if the speaker moves. The H:rvl]\! l parameters of the acoustic transfer function are estimated by HMJ\.I sep­ aration [2]. The method is obtained thro喝h the reverse of the process of HMM composition， where the model parameters are estimated by maximizing the likelihood of adaptation data uttered frol11 an unknown posit.ion. Therefore， measurement of impulse responses is not re­ quired. In this paper， we record the speech of a distant mov ing speaker in reaJ environments. The results of ex periments for the speech of a distant moving speaker clarified the effectiveness of HMM composition and sep­ aratíon. In hands-free speech recognition， one of the key issues as regards practical use is the developm't�nt of a tech­ nology that allows accurate recognition of noisy and re­ verberant speech. Many methods have been presented for solving problems caused by additíve noise and con­ volutionaJ distortion in robust speech recognition. Two common examples of such methods are the speech en­ hancement and model compensation approaches. For the speech enhancement approach， spectraJ subtrac­ tion for additive noise and cepstral mean normalization for convolutional distortion have been proposed (e.g.， [3，4]). For the model compensation approach， the con・ ventionaJ multi-template technique， model adaptation (eι [5， 6 ]) and model (decomposition n methods (eι [1， 7， 8， 9， 10]) have been developed We applied HMM composition to the recognition of speech contaminated not only by additive noise but also by the reverberation of the room [1]. \Ve aJso proposed HMM separation for estimating the HM乱1 parameters of an acoustic transfer function [2]. The model parameters are estimated by maximizing the likelihood of adaptation data uttered from an unknown position. This paper desc1'ibes the performance of the HMM composition and separation for recognition of the speech of a distant moving speaker. The speech of the distant moving speaker is recognized by …