Pitch perception and frequency-following responses elicited by lexical-tone chimeras

Deficits in neural encoding of speech in preterm infants

Preterm children show developmental cognitive and language deficits that can be subtle and sometimes undetectable until later in life. Studies of brain development in children who are born preterm have largely focused on vascular and gross anatomical characteristics rather than pathophysiological processes that may contribute to these developmental deficits. Neural encoding of speech as reflected in EEG recordings is predictive of future language development and could provide insights into those pathophysiological processes. We recorded EEG from 45 preterm (≤ 34 weeks of gestation) and 45 term (≥ 38 weeks) Chinese-learning infants 0-12 months of (corrected) age during natural sleep. Each child listened to three speech stimuli that differed in lexically meaningful pitch (2 native and 1 non-native speech categories). EEG measures associated with synchronization and gross power of the frequency following response (FFR) were examined. ANCOVAs revealed no main effect of stimulus nativeness but main effects of age, consistent with earlier studies. A main effect of prematurity also emerged, with synchronization measures showing stronger group differences than power. By detailing differences in FFR measures related to synchronization and power, this study brings us closer to identifying the pathophysiological pathway to often subtle language problems experienced by preterm children.

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k -nearest neighbors, support vector machines) and an unsupervised model ( k -means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.

Separating the Novel Speech Sound Perception of Lexical Tone Chimeras From Their Auditory Signal Manipulations: Behavioral and Electroencephalographic Evidence

Previous research has shown the novelty of lexical-tone chimeras (artificially constructed speech sounds created by combining normal speech sounds of a given language) to native speakers of the language from which the chimera components were drawn. However, the source of such novelty remains unclear. Our goal in this study was to separate the effects of chimeric tonal novelty in Mandarin speech from the effects of auditory signal manipulations. We recruited 20 native speakers of Mandarin and constructed two sets of lexical-tone chimeras by interchanging the envelopes and fine structures of both a falling/yi⁴/and a rising/yi²/Mandarin tone through 1, 2, 3, 4, 6, 8, 16, 32, and 64 auditory filter banks. We conducted pitch-perception ability tasks via a two-alternative, forced-choice paradigm to produce behavioral (versus physiological) pitch perception data. We also obtained electroencephalographic measurements through the scalp-recorded frequency-following response (FFR). Analyses of variances and post hoc Greenhouse-Geisser procedures revealed that the differences observed in the participants' reaction times and FFR measurements were attributable primarily to chimeric novelty rather than signal manipulation effects. These findings can be useful in assessing neuroplasticity and developing speech-processing strategies.

Subcortical frequency-coding errors are linked to speaker-variability intolerance in normal-hearing adults

Processing speaker-specific information is an important task in daily communication. This study examined how fundamental frequency (F0) cues were encoded at the subcortical level, as reflected by scalp-recorded frequency-following responses, and their relationship with the listener's ability in processing speech stimuli produced by multiple speakers. By using Mandarin tones with distinctive F0 contours, the results indicated that subcortical frequency-coding errors were significantly correlated with the listener's speaker-variability intolerance for both percent correct and reaction time measures. These findings lay a foundation to help improve the understanding of how speaker information is processed in individuals with normal and impaired auditory systems.