# Learning spectro-temporal features with 3D CNNs for speech emotion recognition

arXiv: https://arxiv.org/abs/1708.05071 · HTML: https://mlsanity.com/paper/1708.05071/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Sequential End-to-End Intent and Slot Label Classification and Localization](https://mlsanity.com/paper/2106.04660/) (2021)
- [Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition](https://mlsanity.com/paper/2108.03818/) (2021)

## Abstract (arXiv)

In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed 3D CNNs simultaneously extract short-term and long-term spectral features with a moderate number of parameters. We evaluated our proposed and other state-of-the-art methods in a speaker-independent manner using aggregated corpora that give a large and diverse set of speakers. We found that 1) shallow temporal and moderately deep spectral kernels of a homogeneous architecture are optimal for the task; and 2) our 3D CNNs are more effective for spectro-temporal feature learning compared to other methods. Finally, we visualised the feature space obtained with our proposed method using t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct clusters of emotions.