Quantifying the perceptual value of lexical and non-lexical channels in speech
Not on the map. Why?
Abstract (arXiv)
Speech is a fundamental means of communication that can be seen to provide two channels for transmitting information: the lexical channel of which words are said, and the non-lexical channel of how they are spoken. Both channels shape listener expectations of upcoming communication; however, directly quantifying their relative effect on expectations is challenging. Previous attempts require spoken variations of lexically-equivalent dialogue turns or conspicuous acoustic manipulations. This paper introduces a generalised paradigm to study the value of non-lexical information in dialogue across unconstrained lexical content. By quantifying the perceptual value of the non-lexical channel with both accuracy and entropy reduction, we show that non-lexical information produces a consistent effect on expectations of upcoming dialogue: even when it leads to poorer discriminative turn judgements than lexical content alone, it yields higher consensus among participants.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 47 references that are in the map. First posted on arXiv 7 Jul 2023.
Builds on
4 of 47 references.
References · 4
- 2020On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behaviorcited by 115
- 2021Revisiting the Uniform Information Density Hypothesiscited by 63
- 2022On the probability-quality paradox in language generationcited by 16
- 2021It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication systemcited by 6
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learningcited by 4
- 2023ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formationcited by 3
- 2026Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agentscited by 0
- 2026Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agents10 Mar 2026
- 2025Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning3 Jun 2025
- 2023ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation17 Oct 2023