Emotion Identification from raw speech signals using DNNs

We investigate a number of Deep Neural Network (DNN) architectures for emotion identification with the IEMOCAP database. First we compare different feature extraction front ends: we compare high-dimensional MFCC input (equivalent to filterbanks), versus frequency-domain and time-domain approaches to learning filters as part of the network. We obtain the best results with the time-domain filter-learning approach. Next we investigated different ways to aggregate information over the duration of an utterance. We tried approaches with a single label per utterance with time aggregation inside the network; and approaches where the label is repeated for each frame. Having a separate label per frame seemed to work best, and the best architecture that we tried interleaves TDNN-LSTM with time-restricted self-attention, achieving a weighted accuracy of 70.6%, versus 61.8% for the best previously published system which used 257-dimensional Fourier log energies as input.