We present the joint task of incremental disfluency detection and utterance segmentation and a simple deep learning system which performs it on transcripts and ASR results. We show how the constraints of the two tasks interact. Our joint-task system outperforms the equivalent individual task systems, provides competitive results and is suitable for future use in conversation agents in the psychiatric domain.
On the other hand, REF tackle the task of joint disfluency prediction and utterance segmentation, and demonstrate that the two tasks interact and thus are better approached jointly.
Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech
EACL
Computer Science