Optimized recurrent neural network based emotion recognition using speech for Assamese language

Abstract

This paper deals with the design and development of a dataset for emotion recognition in Assamese language and its analysis using Machine Learning. This dataset comprises of spoken sentences in Assamese language which relates 7 different emotions of the speakers. It also focusses on the analysis of the dataset with Deep Learning architectures and classification algorithm like Recurrent Neural Network (RNN). In total these 7 emotions included calm/neutral, anger, sad, fear, disgust, happy and surprise. The performance of RNN is improved using Representation Learning where training of the model is done using the glottal flow signal to investigate the effect of speaker and phonetic invariant features on classification performance. The speech samples were experimented with different combinations of features and a variety of results were obtained for each of them. In this paper 2 experiments are performed to improve the performance of RNN training. The first one is the representation learning where the training is modelled on the glottal flow signal which is used to investigate the effect of the speaker and phonetic distinct features on the performance of the classification. The second is the transfer learning based RNN training which is done on the valence and activation that is adapted to a 7-category emotion classification. On the Assamese dataset the experimented approach results in a performance which is comparable and similar to the existing state of art systems for emotion recognition using speech.