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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.

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 …