Disfluency detection models for natural conversational voice systems

Abstract

A method includes receiving a sequence of acoustic frames characterizing one or more utterances. At each of a plurality of output steps, the method also includes generating, by an encoder network of a speech recognition model, a higher order feature representation for a corresponding acoustic frame of the sequence of acoustic frames, generating, by a prediction network of the speech recognition model, a hidden representation for a corresponding sequence of non-blank symbols output by a final softmax layer of the speech recognition model, and generating, by a first joint network of the speech recognition model that receives the higher order feature representation generated by the encoder network and the dense representation generated by the prediction network, a probability distribution that the corresponding time step corresponds to a pause and an end of speech.