Speech emotion recognition with skew-robust neural networks

Abstract

We propose a neural-network training algorithm that is robust to data imbalance in classification. In our proposed algorithm, weights are introduced to training examples, effectively modifying the trajectory traversed in the parameter space during the learning process. Furthermore, the proposed algorithm would reduce to the normal stochastic gradient decent learning if the data is balanced. On the FAU-Aibo database, which is known to be used in Interspeech Emotion Challenge, the proposed method achieves an unweighted average (UA) recall rate of 45.3% on the 5-class speech emotion recognition task. Within the static modeling framework, where each example is represented as a fixed-length vector, this performance is one of the best performance ever achieved on the 5-class task.