Recognition and Classification of Human Emotion from Audio

In this paper, the audio emotion recognition system is proposed that uses a mixture of rule-based and machine learning techniques to improve the recognition efficacy in the audio paths. The audio path is designed using a combination of input prosodic features (pitch, log-energy, zero crossing rates and Teager energy operator) and spectral features (Mel-scale frequency cepstral coefficients). Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. After the MFCC feature extraction, these features are passed to three parallel sub-paths which use feature extraction and classification techniques (i.e. BDPCA+LSLDA+RBF). In addition, Naïve Bays and SVM classifier are presented with BDPCS and LSLDA for evaluation of emotion. The extracted audio features are passed into an audio feature level fusion module that uses a set of rules to determine the most likely emotion contained in the audio signal. The performances of the proposed audio path and the final system are evaluated on standard databases of audio clips extracted from the video. Keywords— Emotion recognition, audio-visual processing, rule-based, machine learning, multimodal system