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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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Relating dynamic brain states to dynamic machine states : Human and machine solutions to the speech recognition problem. / Wingfield, Cai; Su, Li; Liu, Xunying; Zhang, Chao; Woodland, Phil; Thwaites, Andrew; Fonteneau, Elisabeth; Marslen-Wilson, William D.

In: PLoS Computational Biology, Vol. 13, No. 9, e1005617, 25.09.2017.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wingfield, C, Su, L, Liu, X, Zhang, C, Woodland, P, Thwaites, A, Fonteneau, E & Marslen-Wilson, WD 2017, 'Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem', PLoS Computational Biology, vol. 13, no. 9, e1005617. https://doi.org/10.1371/journal.pcbi.1005617

APA

Wingfield, C., Su, L., Liu, X., Zhang, C., Woodland, P., Thwaites, A., Fonteneau, E., & Marslen-Wilson, W. D. (2017). Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem. PLoS Computational Biology, 13(9), [e1005617]. https://doi.org/10.1371/journal.pcbi.1005617

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Author

Wingfield, Cai ; Su, Li ; Liu, Xunying ; Zhang, Chao ; Woodland, Phil ; Thwaites, Andrew ; Fonteneau, Elisabeth ; Marslen-Wilson, William D. / Relating dynamic brain states to dynamic machine states : Human and machine solutions to the speech recognition problem. In: PLoS Computational Biology. 2017 ; Vol. 13, No. 9.

Bibtex

@article{1a3017f188234915bde4e5fd28032aed,
title = "Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem",
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.",
author = "Cai Wingfield and Li Su and Xunying Liu and Chao Zhang and Phil Woodland and Andrew Thwaites and Elisabeth Fonteneau and Marslen-Wilson, {William D}",
year = "2017",
month = sep,
day = "25",
doi = "10.1371/journal.pcbi.1005617",
language = "English",
volume = "13",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "9",

}

RIS

TY - JOUR

T1 - Relating dynamic brain states to dynamic machine states

T2 - Human and machine solutions to the speech recognition problem

AU - Wingfield, Cai

AU - Su, Li

AU - Liu, Xunying

AU - Zhang, Chao

AU - Woodland, Phil

AU - Thwaites, Andrew

AU - Fonteneau, Elisabeth

AU - Marslen-Wilson, William D

PY - 2017/9/25

Y1 - 2017/9/25

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1005617

DO - 10.1371/journal.pcbi.1005617

M3 - Journal article

C2 - 28945744

VL - 13

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 9

M1 - e1005617

ER -