  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.
