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.

This study investigates how selective auditory attention influences the lexical speech segmentation process to phonemes and words in a two competing speaker scenario. Using EEG recordings from 20 participants, we applied temporal response function (TRF) analysis to distinguish attention-driven neural activity to phoneme and word onsets for the attended and ignored speech stream separately. Our results reveal distinct attention effects for phoneme and word onsets. Phoneme onsets elicited significant selective attention effects at an early (18-94 ms, P1), middle (186-252 ms, P2), and late time window (302-382 ms, N2). In contrast, word onsets showed attention effects only at a middle (192-280 ms, P2) and late (348-386 ms, N2) time window, occurring slightly later than phoneme-related effects. Prediction accuracy analyses demonstrated stronger model performance for the attended speech stream across all models, with notable improvements in prediction accuracy from word to phoneme and combined word & phoneme model. These findings are in accordance with both hierarchical and parallel processing frameworks, where selective attention enhances lexical segmentation for attended speech, improving prediction accuracy. Early attention effects observed for phoneme onsets underscore their role in low-level speech processing, while late attention effects for word onsets may reflect higher-level processing. This study highlights the importance of selective attention in neural speech tracking and provides insights into auditory processing mechanisms underlying speech comprehension in complex acoustic environments.

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.

Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.