Abstract
Automatically detecting emotional state in human speech, which plays an effective role in areas of human machine interactions, has been a difficult task for machine learning algorithms. Previous work for emotion recognition have mostly focused on the extraction of carefully hand-crafted and tailored features. Recently, spectrogram representations of emotion speech have achieved competitive per- formance for automatic speech emotion recognition. In this work we propose a method to tackle the problem of deep features, herein denoted as deep spectrum features, extraction from the spectrogram by leveraging Attention-based Bidirectional Long Short-Term Mem- ory Recurrent Neural Networks with fully convolutional networks. The learned deep spectrum features are then fed into a deep neural network (DNN) to predict the final emotion. The proposed model is then evaluated on the Interactive Emotional Dyadic Motion Cap- ture (IEMOCAP) dataset to validate its effectiveness. Promising results indicate that our deep spectrum representations extracted from the proposed model perform the best, 65.2% for weighted accuracy and 68.0% for unweighted accuracy when compared to other existing methods. We then compare the performance of our deep spectrum features with two standard acoustic feature repre- sentations for speech-based emotion recognition. When combined with a support vector classifier, the performance of the deep feature representations extracted are comparable with the conventional features. Moreover, we also investigate the impact of different fre- quency resolutions of the input spectrogram on the performance of the system.
