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- W2964172015 abstract "Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model." @default.
- W2964172015 created "2019-07-30" @default.
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- W2964172015 date "2019-01-01" @default.
- W2964172015 modified "2023-10-16" @default.
- W2964172015 title "Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection" @default.
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- W2964172015 doi "https://doi.org/10.18653/v1/n19-1008" @default.
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