Which Prosodic Features Matter Most for Pragmatics?
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Abstract (arXiv)
We investigate which prosodic features matter most in conveying prosodic functions. We use the problem of predicting human perceptions of pragmatic similarity among utterance pairs to evaluate the utility of prosodic features of different types. We find, for example, that duration-related features are more important than pitch-related features, and that utterance-initial features are more important than utterance-final features. Further, failure analysis indicates that modeling using pitch features only often fails to handle important pragmatic functions, and suggests that several generally-neglected acoustic and prosodic features are pragmatically significant, including nasality and vibrato. These findings can guide future basic research in prosody, and suggest how to improve speech synthesis evaluation, among other applications.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 23 Aug 2024.
Builds on
10 of 38 references.
References · 10
- 2023Seamless: Multilingual Expressive and Streaming Speech Translationcited by 190
- 2022On the Utility of Self-supervised Models for Prosody-related Taskscited by 42
- 2020Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshopcited by 15
- 2023Translatotron 3: Speech to Speech Translation with Monolingual Datacited by 12
- 2022Disentangling Prosody Representations with Unsupervised Speech Reconstructioncited by 8
- 2023CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesiscited by 8
- 2024Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Modelscited by 7
- 2023Towards cross-language prosody transfer for dialogcited by 5
- 2024A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterancescited by 2
- 2023Controllable Prosody Generation With Partial Inputscited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.