ADAPTIVE END-OF-UTTERANCE TIMEOUT FOR REAL-TIME SPEECH RECOGNITION

摘要

Real-time speech recognition systems extend an end-of-utterance timeout period in response to the presence of a disfluency at the end of speech, and by so doing avoid cutting off speakers mid-sentence. Approaches to detecting disfluencies include the application of disfluency n-gram language models, acoustic models, prosody models, and phrase spotting. Explicit pause phrases can also be detected to extend sentence parsing until relevant semantic information is gathered from the speaker or another voice. Disfluency models can be trained such as by searching by successive deletion of tokens, phonemes, or acoustic segments to convert sentences that cannot be parsed into ones that can. Disfluency-based timeout adaptation is applicable to safety-critical systems.