Agmon Galit, Jaeger Manuela, Magen Ella, Pinto Danna, Perelmuter Yuval, Zion Golumbic Elana, Bleichner Martin G
Department of English Literature and Linguistics, Bar-Ilan University, Ramat Gan, Israel.
Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.
Neurobiol Lang (Camb). 2025 Sep 5;6. doi: 10.1162/nol.a.12. eCollection 2025.
Spoken language is central to human communication, influencing cognition, learning, and social interactions. Despite its spontaneous nature, characterized by disfluencies, fillers, self-corrections and irregular syntax, it effectively serves its communicative purpose. Understanding how the brain processes natural language offers valuable insights into the neurobiology of language. Recent neuroscience advancements allow us to study neural processes in response to ongoing speech, requiring detailed, time-locked descriptions of speech material to capture the nuances of spoken language. While there are many speech-to-text tools available, obtaining a time-locked true verbatim transcript, reflecting everything that was uttered, requires additional effort to achieve an accurate representation. We demonstrate the challenges involved in the process of obtaining time-resolved annotation of spontaneous speech, by presenting two semi-automatic pipelines, developed for German and Hebrew but adaptable to other languages. The outputs of these pipelines enable analyses of the neural representation and processing of key linguistic features. We discuss the methodological challenges and opportunities posed by current state-of-the-art pipelines, and advocate for new lines of natural language processing research aimed at advancing our understanding of how the brain processes everyday language.
口语是人类交流的核心，影响着认知、学习和社会互动。尽管口语具有自发性，其特点是存在不流畅、填充词、自我修正和不规则语法，但它有效地实现了其交流目的。了解大脑如何处理自然语言为语言神经生物学提供了宝贵的见解。最近神经科学的进展使我们能够研究对正在进行的言语做出反应的神经过程，这需要对言语材料进行详细的、时间锁定的描述，以捕捉口语的细微差别。虽然有许多语音转文本工具可用，但要获得反映所有话语的时间锁定的逐字记录，需要付出额外的努力才能实现准确的呈现。我们通过展示为德语和希伯来语开发但可适应其他语言的两个半自动管道，来说明获取自发语音时间分辨注释过程中所涉及的挑战。这些管道的输出能够分析关键语言特征的神经表征和处理。我们讨论了当前最先进管道所带来的方法学挑战和机遇，并倡导开展新的自然语言处理研究方向，以增进我们对大脑如何处理日常语言的理解。
