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  From birdsong to human speech recognition: Bayesian inference on a hierarchy of nonlinear dynamical systems

Yildiz, B., von Kriegstein, K., & Kiebel, S. J. (2013). From birdsong to human speech recognition: Bayesian inference on a hierarchy of nonlinear dynamical systems. PLoS Computational Biology, 9(9): e1003219. doi:10.1371/journal.pcbi.1003219.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-598C-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-8722-E
Genre: Journal Article

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© 2013 Yildiz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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 Creators:
Yildiz, Burak1, 2, Author              
von Kriegstein, Katharina3, 4, Author              
Kiebel, Stefan J.1, 5, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department d'etudes cognitives, École normale supérieure, Paris, France, ou_persistent22              
3Max Planck Research Group Neural Mechanisms of Human Communication, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634556              
4Department of Psychology, Humboldt University Berlin, Germany, ou_persistent22              
5Hans Berger Clinic for Neurology, Jena University Hospital, Germany, ou_persistent22              

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 Abstract: Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

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Language(s): eng - English
 Dates: 2013-05-232013-07-272013-09-12
 Publication Status: Published online
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1003219
PMID: 24068902
PMC: PMC3772045
Other: Epub 2013
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 9 (9) Sequence Number: e1003219 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1