2019
INTERSPEECH
INTERSPEECH 2019
Disfluencies and Human Speech Transcription Errors
Abstract
This paper explores contexts associated with errors in transcription of spontaneous speech, shedding light on human perception of disfluencies and other conversational speech phenomena. A new version of the Switchboard corpus is provided with disfluency annotations for careful speech transcripts, together with results showing the impact of transcription errors on evaluation of automatic disfluency detection.
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio