Learning spectro-temporal features with 3D CNNs for speech emotion recognition
Abstract
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.
Cite
@article{arxiv.1708.05071,
title = {Learning spectro-temporal features with 3D CNNs for speech emotion recognition},
author = {Jaebok Kim and Khiet P. Truong and Gwenn Englebienne and Vanessa Evers},
journal= {arXiv preprint arXiv:1708.05071},
year = {2017}
}
Comments
ACII, 2017, San Antonio