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Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

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  • Cai Wingfield
  • Li Su
  • Xunying Liu
  • Chao Zhang
  • Phil Woodland
  • Andrew Thwaites
  • Elisabeth Fonteneau
  • William D Marslen-Wilson
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Article numbere1005617
<mark>Journal publication date</mark>25/09/2017
<mark>Journal</mark>PLoS Computational Biology
Issue number9
Volume13
Number of pages25
Publication StatusPublished
<mark>Original language</mark>English

Abstract

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.