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Modelling automatic detection of prosodic boundaries for Brazilian Portuguese spontaneous speech


Prosodic boundaries
Automatic detection
Spontaneous speech

How to Cite

Raso T, Teixeira B, Barbosa P. Modelling automatic detection of prosodic boundaries for Brazilian Portuguese spontaneous speech. J. of Speech Sci. [Internet]. 2020 Sep. 9 [cited 2024 May 20];9(00):105-28. Available from:


Speech 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.


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Copyright (c) 2020 Tommaso Raso, Bárbara Teixeira, Plínio Barbosa


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