A new feature analysis method for robust ASR in reverberant environments based on the harmonic structure of speech

This article proposes a new signal analysis method for automatic speech recognition designed to aim high robustness against distortions caused by room reverberation. The method is initially named Harmonicity based Feature Analysis (HFA) and implements the following three ideas: (i) reconstruction of a spectrum from the harmonic components (assumed to be undistorted) of a voiced speech spectrum. (ii) suppression of disturbing reverberation in unvoiced spectra coming from previous voiced sections. (iii) high frequency regions are not affected by HFA since they have negligible effect on the recognition rate. HFA works on the basis of fundamental frequency estimation and voiced/unvoiced decision. Evaluation results show significant improvement of the recognition performance over a wide range of reverberant conditions while using HFA in connection with reverberant training. Apart from good performance, advantages of HFA compared to state of the art dereverberation approaches are real time processing (no adaptation time) and robustness against changes of the room impulse response.