Missing Feature Speech Recognition using Dereverberation and Echo Suppression in Reverberant Environments

This paper describes an algorithm that efficiently segregates desired speech features from spatially-separated interfering sources in reverberant environments. Although most binaural segregation techniques successfully remove interference components in the absence of reverberation, source segregation in reverberant environments remains a challenging problem. In order to reduce the effects of reverberation, we present a method that dereverberates input signals before they are segregated. The dereverberation filter is estimated from the autocorrelation of the observations and primarily deals with early reflections, while late reflections are effectively suppressed by an inhibitory mechanism that estimates their relative contribution in each time-frequency segment. Information about the salience of the target in a given time-frequency segment based on source separation is combined with the corresponding information based on reverberation suppression through the use of a continually-variable weighting function or mask. Use of the novel reverberation processing results in a relative decrease in WER of 11.5% to 20.9% and use of the combined approaches reduces relative WER by as much as 65.3%.