Speech Emotion Recognition using LSTM and RNN

Deep learning methods are being applied in various recognition tasks such as image, speech, and music recognition. Convolutional Neural Networks (CNNs) especially show remarkable recognition performance for computer vision tasks. In addition, Recurrent Neural Networks (RNNs) and Long shortterm memory (LSTM) show considerable success in many sequential data processing tasks. In this study, we investigate the result of the Speech Emotion Recognition (SER) algorithm based on LSTM-RNN trained using an emotional speech database. The main goal of our work is to propose a SER method based on concatenated CNNs and . By applying the propose methods to an emotional speech database, the classification result was verified to have better accuracy than that achieved using conventional classification methods. As Emotion recognition from speech signals is an important but challenging component of Human Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized or speech-based emotion recognition. The review covers databases used, emotions extracted contributions made toward speech emotion recognition and limitations related to it.