Adversarial Data Augmentation Network for Speech Emotion Recognition

Abstract

Insufficient data is a common issue in training deep learning models. With the introduction of generative adversarial networks (GANs), data augmentation has become a promising solution to this problem. This paper investigates whether data augmentation can help improve speech emotion recognition. Unlike conventional GANs, we train a GAN with an autoencoder, where the input to the discriminator comes from the bottleneck layer of the autoencoder and the output of the generator. The synthetic samples can be obtained from the decoder, using the output of the generator as the decoder's input. The combined network, namely adversarial data augmentation network (ADAN), can generate samples that share common latent representation with the real data. Evaluations on EmoDB and IEMOCAP show that using OpenSmile features as input, the ADAN can produce augmented data that make an ordinary SVM classifier outperforms an RNN classifier with local attention and make a DNN competitive to some state-of-the art systems.