Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition

2020-04-06 · Bagus Tris Atmaja, Masato Akagi

Modern deep learning architectures are ordinarily performed on high-performance computing facilities due to the large size of the input features and complexity of its model. This paper proposes traditional multilayer perceptrons (MLP) with deep layers and small input size to tackle that computation requirement limitation. The result shows that our proposed deep MLP outperformed modern deep learning architectures, i.e., LSTM and CNN, on the same number of layers and value of parameters. The deep MLP exhibited the highest performance on both speaker-dependent and speaker-independent scenarios on IEMOCAP and MSP-IMPROV corpus.

📄 PDF Abstract BibTeX arXiv:2004.02355

Code (1)

bagustris/deep_mlp_ser 공식 구현 tf

Tasks

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