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.Our work outlines a semi-automatic pipeline for annotating natural speech, developed for German and Hebrew but adaptable to other languages, for creating temporally precise time-courses describing key linguistic features of continuous speech, which can be used to analyze their neural representation and level of processing. We discuss the methodological challenges and opportunities this presents, for improving our understanding of how the brain processes everyday language.

Spontaneous real-life speech is imperfect in many ways. It contains disfluencies and ill-formed utterances and has a highly variable rate. When listening to spontaneous speech, the brain needs to contend with these features in order to extract the speaker’s meaning. Here, we studied how the neural response is affected by four specific factors that are prevalent in spontaneous colloquial speech: (1) the presence of fillers, (2) the need to detect syntactic boundaries in disfluent speech, and (3) variability in speech rate. Neural activity was recorded (using electroencephalography) from individuals as they listened to an unscripted, spontaneous narrative, which was analyzed in a time-resolved fashion to identify fillers and detect syntactic boundaries. When considering these factors in a speech-tracking analysis, which estimates a temporal response function (TRF) to describe the relationship between the stimulus and the neural response it generates, we found that the TRF was affected by all of them. This response was observed for lexical words but not for fillers, and it had an earlier onset for opening words vs. closing words of a clause and for clauses with slower speech rates. These findings broaden ongoing efforts to understand neural processing of speech under increasingly realistic conditions. They highlight the importance of considering the imperfect nature of real-life spoken language, linking past research on linguistically well-formed and meticulously controlled speech to the type of speech that the brain actually deals with on a daily basis.