Phonetic acquisition in cortical dynamics, a computational approach.
Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in whic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0217966 |
| _version_ | 1819148496864804864 |
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| author | Dario Dematties Silvio Rizzi George K Thiruvathukal Alejandro Wainselboim B Silvano Zanutto |
| author_facet | Dario Dematties Silvio Rizzi George K Thiruvathukal Alejandro Wainselboim B Silvano Zanutto |
| author_sort | Dario Dematties |
| collection | DOAJ |
| description | Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units-such as phonemes-are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of-for example-backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream. |
| first_indexed | 2024-12-22T13:46:39Z |
| format | Article |
| id | doaj.art-f35d288bb1fc4b63976cd23cb1415657 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T13:46:39Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f35d288bb1fc4b63976cd23cb14156572022-12-21T18:23:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01146e021796610.1371/journal.pone.0217966Phonetic acquisition in cortical dynamics, a computational approach.Dario DemattiesSilvio RizziGeorge K ThiruvathukalAlejandro WainselboimB Silvano ZanuttoMany computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units-such as phonemes-are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of-for example-backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream.https://doi.org/10.1371/journal.pone.0217966 |
| spellingShingle | Dario Dematties Silvio Rizzi George K Thiruvathukal Alejandro Wainselboim B Silvano Zanutto Phonetic acquisition in cortical dynamics, a computational approach. PLoS ONE |
| title | Phonetic acquisition in cortical dynamics, a computational approach. |
| title_full | Phonetic acquisition in cortical dynamics, a computational approach. |
| title_fullStr | Phonetic acquisition in cortical dynamics, a computational approach. |
| title_full_unstemmed | Phonetic acquisition in cortical dynamics, a computational approach. |
| title_short | Phonetic acquisition in cortical dynamics, a computational approach. |
| title_sort | phonetic acquisition in cortical dynamics a computational approach |
| url | https://doi.org/10.1371/journal.pone.0217966 |
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