Speech Emotion Recognition Using Residual Phase and MFCC Features

Abstract--The main objective of this research is to develop a speech emotion recognition system using residual phase and MFCC features with autoassociative neural network (AANN). The speech emotion recognition system classifies the speech emotion into predefined categories such as anger, fear, happy, neutral or sad. The proposed technique for speech emotion recognition (SER) has two phases : Feature extraction, and Classification. Initially, speech signal is given to feature extraction phase to extract residual phase and MFCC features. Based on the feature vectors extracted from the training data, Autoassociative neural network (AANN) are trained to classify the emotions into anger, fear, happy, neutral or sad. Using residual phase and MFCC features the performance of the proposed technique is evaluated in terms of FAR and FRR. The experimental results show that the residual phase gives an equal error rate (EER) of 41.0%, and the system using the MFCC features gives an EER of 20.0%. By combining the both the residual phase and the MFCC features at the matching score level, an EER of 16.0% is obtained. Keyword—Mel frequency cepstral coefficients, Residual phase, Autoassociative neural network, Speech emotion recognition.