It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication system
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Abstract (arXiv)
People convey information extremely effectively through spoken interaction using multiple channels of information transmission: the lexical channel of what is said, and the non-lexical channel of how it is said. We propose studying human perception of spoken communication as a means to better understand how information is encoded across these channels, focusing on the question 'What characteristics of communicative context affect listener's expectations of speech?'. To investigate this, we present a novel behavioural task testing whether listeners can discriminate between the true utterance in a dialogue and utterances sampled from other contexts with the same lexical content. We characterize how perception - and subsequent discriminative capability - is affected by different degrees of additional contextual information across both the lexical and non-lexical channel of speech. Results demonstrate that people can effectively discriminate between different prosodic realisations, that non-lexical context is informative, and that this channel provides more salient information than the lexical channel, highlighting the importance of the non-lexical channel in spoken interaction.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 41 references that are in the map. First posted on arXiv 1 May 2021.
Builds on
7 of 41 references.
References · 7
- 2019Unsupervised speech representation learning using WaveNet autoencoderscited by 90
- 2020Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trendscited by 28
- 2018Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNscited by 12
- 2020Is this Dialogue Coherent? Learning from Dialogue Acts and Entitiescited by 11
- 2018Coherence Models for Dialoguecited by 9
- 2018Unspeech: Unsupervised Speech Context Embeddingscited by 6
- 2020Perception of prosodic variation for speech synthesis using an unsupervised discrete representation of F0cited by 4
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 2.
6 papers.
- 2022A neural prosody encoder for end-ro-end dialogue act classificationcited by 5
- 2023Improving End-to-End SLU performance with Prosodic Attention and Distillationcited by 5
- 2023Quantifying the perceptual value of lexical and non-lexical channels in speechcited by 3
- 2024Joint Learning of Context and Feedback Embeddings in Spoken Dialoguecited by 2
- 2026Real-Time Voice AI Hears but Does Not Listencited by 1
- 2026Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agentscited by 0
- 2026Real-Time Voice AI Hears but Does Not Listen24 Jun 2026
- 2026Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agents10 Mar 2026
- 2024Joint Learning of Context and Feedback Embeddings in Spoken Dialogue11 Jun 2024
- 2023Quantifying the perceptual value of lexical and non-lexical channels in speech7 Jul 2023
- 2023Improving End-to-End SLU performance with Prosodic Attention and Distillation14 May 2023
- 2022A neural prosody encoder for end-ro-end dialogue act classification11 May 2022