A module-based framework to emotion recognition by speech: a case study in clinical simulation (2023)
- Authors:
- Autor USP: UEYAMA, JO - ICMC
- Unidade: ICMC
- DOI: 10.1007/s12652-019-01280-8
- Subjects: INTERAÇÃO USUÁRIO-COMPUTADOR; ESTADOS EMOCIONAIS; ANÁLISE DO COMPORTAMENTO; SAÚDE; COMPUTAÇÃO APLICADA
- Keywords: Emotion; Classification; Cognitive computing
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2023
- Source:
- Título do periódico: Journal of Ambient Intelligence and Humanized Computing
- ISSN: 1868-5137
- Volume/Número/Paginação/Ano: v. 14, p. 15513-15522, 2023
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SAWADA, Luana Okino et al. A module-based framework to emotion recognition by speech: a case study in clinical simulation. Journal of Ambient Intelligence and Humanized Computing, v. 14, p. 15513-15522, 2023Tradução . . Disponível em: https://doi.org/10.1007/s12652-019-01280-8. Acesso em: 28 abr. 2024. -
APA
Sawada, L. O., Mano, L. Y., Torres Neto, J. R., & Ueyama, J. (2023). A module-based framework to emotion recognition by speech: a case study in clinical simulation. Journal of Ambient Intelligence and Humanized Computing, 14, 15513-15522. doi:10.1007/s12652-019-01280-8 -
NLM
Sawada LO, Mano LY, Torres Neto JR, Ueyama J. A module-based framework to emotion recognition by speech: a case study in clinical simulation [Internet]. Journal of Ambient Intelligence and Humanized Computing. 2023 ; 14 15513-15522.[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/s12652-019-01280-8 -
Vancouver
Sawada LO, Mano LY, Torres Neto JR, Ueyama J. A module-based framework to emotion recognition by speech: a case study in clinical simulation [Internet]. Journal of Ambient Intelligence and Humanized Computing. 2023 ; 14 15513-15522.[citado 2024 abr. 28 ] Available from: https://doi.org/10.1007/s12652-019-01280-8 - Contribuições para a rede sem fio
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Informações sobre o DOI: 10.1007/s12652-019-01280-8 (Fonte: oaDOI API)
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