Emotion recognition in speech using MFCC and wavelet features

Venkata Krishna Kishore Kolli · Perabathula Satish

121 citations0/0 files storedIEEE International Advance Computing Conference

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Emotion and Mood Recognition Speech Recognition and Synthesis Music and Audio Processing

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.

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References

2005Toward detecting emotions in spoken dialogs 2002Recognizing emotion in speech 2005The role of intonation in emotional expressions 2001Nonlinear feature based classification of speech under stress 1984Vocal cues to speaker affect: Testing two models 2002Bimodal emotion recognition 2004Audio-visual based emotion recognition-a new approach

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2014A new approach of audio emotion recognition 2015Emotion Detection Using MFCC and Cepstrum Features 2020Egyptian Arabic speech emotion recognition using prosodic, spectral and wavelet features 2017Emotion recognition from speech using MFCC and DWT for security system 2018Speech emotion recognition research: an analysis of research focus 2017Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition 2016Emotion recognition on the basis of audio signal using Naive Bayes classifier

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