Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues
Sushant Kafle, Cissi Ovesdotter Alm, Matt Huenerfauth · 2019
Prosodic cues in conversational speech aid listeners in discerning a message.We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn.Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings.Word error rate, a traditional metric for evaluating automatic speech recognition (ASR), fails to capture that some words are more important for a system to transcribe correctly than others.We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction.Our model performs competitively against state-ofthe-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.