Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition

  • Ronnypetson da Silva CPQD
  • Valter M. Filho CPqD
  • Mario Souza CPFL Energia

Resumo


Many works that apply Deep Neural Networks (DNNs) to Speech
Emotion Recognition (SER) use single datasets or train and evaluate the models
separately when using multiple datasets. Those datasets are constructed with
specific guidelines and the subjective nature of the labels for SER makes it difficult to obtain robust and general models. We investigate how DNNs learn shared
representations for different datasets in both multi-task and unified setups. We
also analyse how each dataset benefits from others in different combinations
of datasets and popular neural network architectures. We show that the longstanding belief of more data resulting in more general models doesn’t always
hold for SER, as different dataset and meta-parameter combinations hold the
best result for each of the analysed datasets.

Palavras-chave: Speech, emotion, dataset, recognition, deep learning, multitask

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Publicado
20/10/2020
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DA SILVA, Ronnypetson; M. FILHO, Valter; SOUZA, Mario. Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 342-353. DOI: https://doi.org/10.5753/eniac.2020.12141.