Title :
Emotion recognition in speech using MFCC and wavelet features
Author :
Krishna Kishore, K.V. ; Krishna Satish, P.
Author_Institution :
Comput. Sci. & Eng, Vignan Univ., Guntur, India
Abstract :
Recognition of emotions from speech is one of the most important sub domains in the field of affective computing. Six basic emotional states are considered for classification of emotions from speech in this work. In this work, features are extracted from audio characteristics of emotional speech by Mel-frequency Cepstral Coefficient (MFCC), and Subband based Cepstral Parameter (SBC) method. Further these features are classified using Gaussian Mixture Model (GMM). SAVEE audio database is used in this work for testing of Emotions. In the experimental results, SBC method out performs with 70% in recognition compared to 51% of recognition in MFCC algorithm.
Keywords :
Gaussian processes; audio databases; emotion recognition; feature extraction; signal classification; speech recognition; wavelet transforms; GMM; Gaussian mixture model; MFCC feature; Mel frequency cepstral coefficient; SAVEE audio database; SBC method; affective computing; audio characteristics; emotion classification; emotion recognition; emotional state; feature extraction; speech recognition; subband based cepstral parameter; wavelet feature; Accuracy; Emotion recognition; Feature extraction; Mel frequency cepstral coefficient; Speech; Speech recognition; Training; Gaussian Mixture Model (GMM); Mel-frequency Cepstral Coefficient (MFCC); Subband based Cepstral Parameter (SBC);
Conference_Titel :
Advance Computing Conference (IACC), 2013 IEEE 3rd International
Conference_Location :
Ghaziabad
Print_ISBN :
978-1-4673-4527-9
DOI :
10.1109/IAdCC.2013.6514336
