Localizing category-related information in speech with multi-scale analyses.

Measurements of the physical outputs of speech-vocal tract geometry and acoustic energy-are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic sig...

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Autores principales:	Sam Tilsen, Seung-Eun Kim, Claire Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f6886a041f6c4acc9adc938b3350f949
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spelling	oai:doaj.org-article:f6886a041f6c4acc9adc938b3350f9492021-12-02T20:17:23ZLocalizing category-related information in speech with multi-scale analyses.1932-620310.1371/journal.pone.0258178https://doaj.org/article/f6886a041f6c4acc9adc938b3350f9492021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258178https://doaj.org/toc/1932-6203Measurements of the physical outputs of speech-vocal tract geometry and acoustic energy-are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.Sam TilsenSeung-Eun KimClaire WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258178 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sam Tilsen Seung-Eun Kim Claire Wang Localizing category-related information in speech with multi-scale analyses.
description	Measurements of the physical outputs of speech-vocal tract geometry and acoustic energy-are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.
format	article
author	Sam Tilsen Seung-Eun Kim Claire Wang
author_facet	Sam Tilsen Seung-Eun Kim Claire Wang
author_sort	Sam Tilsen
title	Localizing category-related information in speech with multi-scale analyses.
title_short	Localizing category-related information in speech with multi-scale analyses.
title_full	Localizing category-related information in speech with multi-scale analyses.
title_fullStr	Localizing category-related information in speech with multi-scale analyses.
title_full_unstemmed	Localizing category-related information in speech with multi-scale analyses.
title_sort	localizing category-related information in speech with multi-scale analyses.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f6886a041f6c4acc9adc938b3350f949
work_keys_str_mv	AT samtilsen localizingcategoryrelatedinformationinspeechwithmultiscaleanalyses AT seungeunkim localizingcategoryrelatedinformationinspeechwithmultiscaleanalyses AT clairewang localizingcategoryrelatedinformationinspeechwithmultiscaleanalyses
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Localizing category-related information in speech with multi-scale analyses.

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