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Solera-Ureña, R., Moniz, H., Batista, F., Cabarrão, V., Pompili, A., Astudillo, R....Trancoso, I. (2019). Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas. Revista da Associação Portuguesa de Linguística. 5, 348-364

R. Solera-Ureña et al.,  "Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas", in Revista da Associação Portuguesa de Linguística, no. 5, pp. 348-364, 2019

@article{solera-ureña2019_1714635528765,
	author = "Solera-Ureña, R. and Moniz, H. and Batista, F. and Cabarrão, V. and Pompili, A. and Astudillo, R. and Trancoso, I.",
	title = "Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas",
	journal = "Revista da Associação Portuguesa de Linguística",
	year = "2019",
	volume = "",
	number = "5",
	doi = "10.26334/2183-9077/rapln5ano2019a23",
	pages = "348-364",
	url = "http://ojs.apl.pt/index.php/RAPL/article/view/21"
}

TY  - JOUR
TI  - Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas
T2  - Revista da Associação Portuguesa de Linguística
IS  - 5
AU  - Solera-Ureña, R.
AU  - Moniz, H.
AU  - Batista, F.
AU  - Cabarrão, V.
AU  - Pompili, A.
AU  - Astudillo, R.
AU  - Trancoso, I.
PY  - 2019
SP  - 348-364
SN  - 2183-9077
DO  - 10.26334/2183-9077/rapln5ano2019a23
UR  - http://ojs.apl.pt/index.php/RAPL/article/view/21
AB  - Automatic personality analysis has gained great attention in the last years as a fundamental dimension in human-machine interactions. However, the development of this technology in some domains, such as the classification of children’s personality, has been hindered by the limited number and size of the available speech corpora due to ethical concerns on collecting such corpora. To circumvent the lack of data, we have investigated the application of a semi-supervised training approach that makes use of heterogeneous (age and language mismatches) and partially non-labelled data sets. Namely, preliminary personality models trained using a small labelled data set with French speaking adults are iteratively refined using a larger unlabeled set of Portuguese children’s speech, whereas a labelled corpus of Portuguese children is used for evaluation. We also investigated speech representations based on prior linguistic knowledge on acoustic-prosodic clues for personality classification tasks and have analysed their relevance in the assessment of each personality trait. The results point out to the potential of applying semi-supervised learning approaches with heterogeneous data sets to overcome the lack of labelled data in under-resourced domains, and to the existence of acousticprosodic clues shared by speakers with different languages and ages, which allows for the classification of personality independently of these variables.
ER  -