Robust word recognition using threaded spectral peaks

A novel technique which characterizes the position and motion of dominant spectral peaks in speech, significantly reduces the error-rate of an HMM-based word-recognition system. The technique includes approximate auditory filtering, temporal adaptation, identification of local spectral peaks in each frame, grouping of neighboring peaks into threads, estimation of frequency derivatives, and slowly updating approximations of the threads and their derivatives. This processing provides a frame-based speech representation which is both dependent on perceptually salient aspects of the frame´s immediate context, and is well-suited to segmentally-stationary statistical characterization. In noise, the representation reduces the error-rate obtained with standard Mel-filter-based feature vectors by as much as a factor of 4, and provides improvements over other common feature-vector manipulations