# Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2004.02355 · HTML: https://mlsanity.com/paper/2004.02355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [A Pre-trained Audio-Visual Transformer for Emotion Recognition](https://mlsanity.com/paper/2201.09165/) (2022)

## Abstract (arXiv)

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.