Modelling automatic detection of prosodic boundaries for Brazilian Portuguese spontaneous speech

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dc.creatorTommaso Raso
dc.creatorBárbara Teixeira
dc.creatorPlinio Barbosa
dc.date.accessioned2023-09-19T21:34:29Z
dc.date.accessioned2025-09-08T22:53:41Z
dc.date.available2023-09-19T21:34:29Z
dc.date.issued2020-09-09
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.20396/joss.v9i00.14957
dc.identifier.issn2236-9740
dc.identifier.urihttps://hdl.handle.net/1843/58800
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofJournal of Speech Sciences
dc.rightsAcesso Aberto
dc.subjectAtos de fala (Linguística)
dc.subjectLíngua portuguesa - Fonologia
dc.subject.otherProsodic boundaries
dc.subject.otherAutomatic detection
dc.subject.otherSpontaneous speech
dc.titleModelling automatic detection of prosodic boundaries for Brazilian Portuguese spontaneous speech
dc.typeArtigo de periódico
local.citation.epage128
local.citation.spage105
local.citation.volume9
local.description.resumoSpeech is segmented into intonational units marked by prosodic boundaries. This segmentation is claimed to have important consequences on syntax, information structure and cognition. This work aims both to investigate the phonetic-acoustic parameters that guide the production and perception of prosodic boundaries, and to develop models for automatic detection of prosodic boundaries in male monological spontaneous speech of Brazilian Portuguese. Two samples were segmented into intonational units by two groups of trained annotators. The boundaries perceived by the annotators were tagged as either terminal or non-terminal. A script was used to extract 111 phonetic-acoustic parameters along speech signal in a right and left windows around the boundary of each phonological word. The extracted parameters comprise measures of (1) Speech rate and rhythm; (2) Standardized segment duration; (3) Fundamental frequency; (4) Intensity; (5) Silent pause. The script considers as prosodic boundary positions at which at least 50% of the annotators indicated a boundary of the same type. A training of models composed by the parameters extracted by the script was developed; these models, were then improved heuristically. The models were developed from the two samples and from the whole data, both using non-balanced and balanced data. Linear Discriminant Analysis algorithm was adopted to produce the models. The models for terminal boundaries show a much higher performance than those for non-terminal ones. In this paper we: (i) show the methodological procedures; (ii) analyze the different models; (iii) discuss some strategies that could lead to an improvement of our results.
local.identifier.orcidhttps://orcid.org/0000-0002-3446-313X
local.identifier.orcidhttps://orcid.org/0000-0001-6317-3548
local.publisher.countryBrasil
local.publisher.departmentFALE - FACULDADE DE LETRAS
local.publisher.initialsUFMG

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