Phonetic acquisition in cortical dynamics, a computational approach
Abstract
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 anymore »
- Authors:
-
[1];
[2];
- Univ. de Buenos Aires, Buenos Aires (Argentina)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Argonne National Lab. (ANL), Lemont, IL (United States); Loyola Univ. Chicago, Chicago, IL (United States)
- Inst. de Ciencias Humanas, Mendoza (Argentina)
- Univ. de Buenos Aires, Buenos Aires (Argentina); Inst. de Biología y Medicina Experimental-CONICET, Ciudad Autónoma de Buenos Aires (Argentina)
- Fraunhofer-Inst. fur Nachrichtentechnik Heinrich-Hertz-Inst. (Germany)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- Argonne National Laboratory - Argonne Leadership Computing Facility; USDOE
- OSTI Identifier:
- 1559970
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 6; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES
Citation Formats
Dematties, Dario, Rizzi, Silvio, Thiruvathukal, George K., Wainselboim, Alejandro, Zanutto, B. Silvano, and Samek, Wojciech. Phonetic acquisition in cortical dynamics, a computational approach. United States: N. p., 2019.
Web. doi:10.1371/journal.pone.0217966.
Dematties, Dario, Rizzi, Silvio, Thiruvathukal, George K., Wainselboim, Alejandro, Zanutto, B. Silvano, & Samek, Wojciech. Phonetic acquisition in cortical dynamics, a computational approach. United States. https://doi.org/10.1371/journal.pone.0217966
Dematties, Dario, Rizzi, Silvio, Thiruvathukal, George K., Wainselboim, Alejandro, Zanutto, B. Silvano, and Samek, Wojciech. Fri .
"Phonetic acquisition in cortical dynamics, a computational approach". United States. https://doi.org/10.1371/journal.pone.0217966. https://www.osti.gov/servlets/purl/1559970.
@article{osti_1559970,
title = {Phonetic acquisition in cortical dynamics, a computational approach},
author = {Dematties, Dario and Rizzi, Silvio and Thiruvathukal, George K. and Wainselboim, Alejandro and Zanutto, B. Silvano and Samek, Wojciech},
abstractNote = {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.},
doi = {10.1371/journal.pone.0217966},
journal = {PLoS ONE},
number = 6,
volume = 14,
place = {United States},
year = {Fri Jun 07 00:00:00 EDT 2019},
month = {Fri Jun 07 00:00:00 EDT 2019}
}
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Adaptation in Hair Cells
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Modality and Topographic Properties of Single Neurons of Cat'S Somatic Sensory Cortex
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Experimental evidence for sparse firing in the neocortex.
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