Evaluating deep learning architectures for Speech Emotion Recognition
Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame based formulation to SER that relies on minimal speech processing and end to end deep learning to model intra utterance dynamics. We use the proposed SER system to empirically explore feed forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state of the art results on the IEMOCAP database for speaker independent SER and present quantitative and qualitative assessments of the models' performances.
Neural Networks