Adaptive dereverberation of speech signals with speaker-position change detection

This paper proposes a method for adaptive speech dereverberation and speaker-position change detection, which have not previously been addressed. Signal transmission channels in rooms are modeled as auto-regressive systems in individual frequency bands. The proposed method adaptively estimates the regression coefficients of this model, which are called room regression coefficients (RRCs). The proposed method has two distinguishing features: (1) The method is based on the weighted recursive least squares algorithm, which enables an efficient RRC-estimate update as well as a fast convergence rate; (2) The method detects changes in speaker position and so can quickly catch up with the sudden channel changes that such position changes cause. Detection is realized by finding time frames where the power of dereverberated speech is anomalously amplified. Experimental results showed that the proposed method attained convergence in 5 seconds and successfully detected changes in speaker position.