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Article
Phonetic acquisition in cortical dynamics, a computational approach
PLoS One
  • Dario Dematties, University of Buenos Aires
  • Silvio Rizzi, Argonne National Laboratory
  • George K. Thiruvathukal, Loyola University Chicago
  • Alejandro Wainselboim, University of Buenos Aires
  • B. Silvano Zanutto, University of Buenos Aires
Document Type
Article
Publication Date
6-7-2019
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 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.

Identifier
10.1371/journal.pone.0217966
Creative Commons License
Creative Commons Attribution-Share Alike 4.0
Citation Information
Dematties D, Rizzi S, Thiruvathukal GK, Wainselboim A, Zanutto BS (2019) Phonetic acquisition in cortical dynamics, a computational approach. PLoS ONE 14(6): e0217966. https://doi.org/10.1371/journal.pone.0217966