Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Abstract

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as sequences of discrete speech sounds, which are used to recognise words. To examine how the human brain appropriately sequences the speech signal, we recorded two-hour magnetoencephalograms from 21 subjects listening to short narratives. Our analyses show that the brain continuously encodes the three most recently heard speech sounds in parallel, and maintains this information long past the sensory input. Each speech sound has a representation that evolves over time, jointly encoding both its phonetic features and time elapsed since onset. This allows the brain to represent the relative order and phonetic content of the phonetic sequence. These dynamic representations are active earlier when phonemes are more predictable, and are sustained longer when lexical identity is uncertain. The flexibility in the dynamics of these representations paves the way for further understanding of how such sequences may be used to interface with higher order structure such as morphemes and words.Bio: Laura Gwilliams received her PhD in Psychology with a focus in Cognitive Neuroscience from New York University in May 2020. Currently she is a post-doctoral researcher at UCSF, using MEG and ECoG data to understand how linguistic structures are parsed and composed while listening to continuous speech. The ultimate goal of Laura's research is to describe speech comprehension in terms of what operations are applied to the acoustic signal; which representational formats are generated and manipulated (e.g. phonetic, syllabic, morphological), and under what processing architecture.