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http://hdl.handle.net/10071/20136
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Title: Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas
Authors: Solera-Ureña, R.
Moniz, H.
Batista, F.
Cabarrão, V.
Pompili, A.
Astudillo, R.
Trancoso, I.
Keywords: Análise paralinguística computacional
Classificação automática de personalidade
Línguas distintas
Faixas etárias diferentes
Pistas acústico-prosódicas
Issue Date: 2019
Publisher: Associação Portuguesa de Linguística e Faculdade de Letras da Universidade do Porto
Abstract: 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.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/20136
DOI: 10.26334/2183-9077/rapln5ano2019a23
ISSN: 2183-9077
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-63642
Appears in Collections:IT-RN - Artigo em revista nacional com arbitragem científica

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