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.

翻译：说话者的身份通过调节感知和预期影响语言理解。本综述探讨了说话者效应，并提出一个整合不同机制视角的语言与说话者加工模型。我们认为，说话者效应源于自下而上的基于感知的过程（由声学-情景记忆驱动）与自上而下的基于预期的过程（由说话者模型驱动）之间的相互作用。研究表明，语言与说话者加工通过多层次概率加工实现功能整合：关于说话者的先验信念在语音、词汇和语义层面调节语言处理，而展开的言语和讯息则持续更新说话者模型，将宽泛的人口统计学先验知识精炼为精确的个体化表征。在此框架内，我们区分了源于个体熟悉性的说话者特异效应与源于社会群体预期的说话者人口统计学效应。本文探讨了说话者效应如何作为评估语言发展和社会认知的指标，并鼓励未来研究将这些发现延伸至新兴的人工智能说话者领域——人工智能代理作为新型社会对话者正在改变人类的沟通方式。