Separation of stop consonants

To extract speech from acoustic interference is a challenging problem. Previous systems based on auditory scene analysis principles deal with voiced speech, but cannot separate unvoiced speech. We propose a novel method to separate stop consonants, which contain significant unvoiced signals, based on their acoustic properties. The method employs onset as the major grouping cue; it first detects stops through onset detection and feature-based Bayesian classification, then groups detected onsets based on onset coincidence. This method is tested with utterances mixed with various types of interference.