Machine learning approaches to analyze speech-evoked neurophysiological responses

Purpose: Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)–based approaches to analyzing speech-evoked neurophysiological responses. Method: Two categories of ML-based approaches are introduced: decoding models, which generate a speech stimulus output using the features from the neurophysiological responses, and encoding models, which use speech stimulus features to predict neurophysiological responses. In this review, we focus on (a) a decoding model classification approach, wherein speech-evoked neurophysiological responses are classified as belonging to 1 of a finite set of possible speech events (e.g., phonological categories), and (b) an encoding model temporal response function approach, which quantifies the transformation of a speech stimulus feature to continuous neural activity. Results: We illustrate the utility of the classification approach to analyze early electroencephalographic (EEG) responses to Mandarin lexical tone categories from a traditional experimental design, and to classify EEG responses to English phonemes evoked by natural continuous speech (i.e., an audiobook) into phonological categories (plosive, fricative, nasal, and vowel). We also demonstrate the utility of temporal response function to predict EEG responses to natural continuous speech from acoustic features. Neural metrics from the 3 examples all exhibit statistically significant effects at the individual level. Conclusion: We propose that ML-based approaches can complement traditional analysis approaches to analyze neurophysiological responses to speech signals and provide a deeper understanding of natural speech and language processing using ecologically valid paradigms in both typical and clinical populations.