Speech Emotion Recognition (SER) & Gender Detection Using Deep Learning
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 for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it. A Multilayer Perceptron (MLP) deep learning model has been described to recognize voice gender. The data set have 3,168 recorded samples of male and female voices. The samples are produced by using acoustic analysis. An MLP deep learning algorithm has been applied to detect gender specific traits.