  This paper proposes a hybrid approach for co-channel speech segregation. HMM
(hidden Markov model) is used to track the pitches of 2 talkers. The resulting
pitch tracks are then enriched with the prominent pitch. The enriched tracks
are correctly grouped using pitch continuity. Medium frame harmonics are used
to extract the second pitch for frames with only one pitch deduced using the
previous steps. Finally, the pitch tracks are input to CASA (computational
auditory scene analysis) to segregate the mixed speech. The center frequency
range of the gamma tone filter banks is maximized to reduce the overlap between
the channels filtered for better segregation. Experiments were conducted using
this hybrid approach on the speech separation challenge database and compared
to the single (non-hybrid) approaches, i.e. signal processing and CASA. Results
show that using the hybrid approach outperforms the single approaches.
