IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
De-noising by soft-thresholding
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
Speech is one of the most natural medium for human communication, which makes it vital to human-robot interaction. In real environments where robots are deployed, distant-talking speech recognition is difficult to realize due to the effects of reverberation. This leads to the degradation of speech recognition and understanding, and hinders a seamless human-robot interaction. To minimize this problem, traditional speech enhancement techniques optimized for human perception are adopted to achieve robustness in human-robot interaction. However, human and machine perceive speech differently: an improvement in speech recognition performance may not automatically translate to an improvement in human-robot interaction experience (as perceived by the users). In this paper, we propose a method in optimizing speech enhancement techniques specifically to improve automatic speech recognition (ASR) with emphasis on the human-robot interaction experience. Experimental results using real reverberant data in a multi-party conversation, show that the proposed method improved human-robot interaction experience in severe reverberant conditions compared to the traditional techniques.