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

fYear

2013

fDate

22-23 Feb. 2013

Firstpage

842

Lastpage

847

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2013 IEEE 3rd International

Conference_Location

Ghaziabad

Print_ISBN

978-1-4673-4527-9

Type

conf

DOI

10.1109/IAdCC.2013.6514336

Filename

6514336