# Speaker effects in language comprehension: An integrative model of language and speaker processing

arXiv: https://arxiv.org/abs/2412.07238 · HTML: https://mlsanity.com/paper/2412.07238/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 238 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023)
- [When a Man Says He Is Pregnant: Event-related Potential Evidence for a Rational Account of Speaker-contextualized Language Comprehension](https://mlsanity.com/paper/2409.17525/) (2024)

## Built on by (most cited first, 4 of 4)

- [Mind the Gap! Static and Interactive Evaluations of Large Audio Models](https://mlsanity.com/paper/2502.15919/) (2025)
- [Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment](https://mlsanity.com/paper/2503.19586/) (2025)
- [Probabilistic adaptation of language comprehension for individual speakers: evidence from neural oscillations](https://mlsanity.com/paper/2502.01299/) (2025)
- [Acoustic-based Gender Differentiation in Speech-aware Language Models](https://mlsanity.com/paper/2509.21125/) (2025)

## Abstract (arXiv)

The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based processes, driven by acoustic-episodic memory, and top-down expectation-based processes, driven by a speaker model. We show that language and speaker processing are functionally integrated through multi-level probabilistic processing: prior beliefs about a speaker modulate language processing at the phonetic, lexical, and semantic levels, while the unfolding speech and message continuously update the speaker model, refining broad demographic priors into precise individualized representations. Within this framework, we distinguish between speaker-idiosyncrasy effects arising from familiarity with an individual and speaker-demographics effects arising from social group expectations. We discuss how speaker effects serve as indices for assessing language development and social cognition, and we encourage future research to extend these findings to the emerging domain of artificial intelligence (AI) speakers, as AI agents represent a new class of social interlocutors that are transforming the way we engage in communication.