Speech stream segregation is presented as a new speech enhancement for
automatic speech recognition.  Two issues are addressed: speech stream
segregation from a mixture of sounds, and interfacing speech stream
segregation with automatic speech recognition.  Speech stream
segregation is modeled as a process of extracting harmonic fragments,
grouping these extracted harmonic fragments, and substituting
non-harmonic residue for non-harmonic parts of groups.  The main
problem in interfacing speech stream segregation with HMM-based speech
recognition is how to improve the degradation of recognition
performance due to spectral distortion of segregated sounds, which is
caused mainly by transfer function of a binaural input.  Our solution
is to re-train the parameters of HMM with training data binauralized
for four directions.  Experiments with 500 mixtures of two women's
utterances of a word showed that the cumulative accuracy of word
recognition up to the 10th candidate of each woman's utterance is, on
average, 75%.