Emotion recognition from Assamese speeches using MFCC features and GMM classifier

Aditya Bihar Kandali · Aurobinda Routray · T.K. Basu

82 citations0/0 files storedIEEE Region 10 Conference (TENCON)

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

Abstract

This paper presents a method based on Gaussian mixture model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as features for emotion recognition from Assamese speeches. For training and testing of the method, data collection is carried out in Jorhat (Assam, India), which consisted of acted speeches of one short emotionally biased sentence repeated 5 times with different styles by 27 speakers (14 Male and 13 female) for training and one long emotional speech by each speaker for testing. The experiments are performed for the cases of (i) text-independent but speaker-dependent and (ii) text-independent and speaker-independent.

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References

2001Emotion recognition in human-computer interaction 2003Speech emotion recognition using hidden Markov models 2001Nonlinear feature based classification of speech under stress 2005Comparing Feature Sets for Acted and Spontaneous Speech in View of Automatic Emotion Recognition 2004Automatic emotional speech classification 2002Automatic statistical analysis of the signal and prosodic signs of emotion in speech 1996Feature analysis and neural network-based classification of speech under stress 2006Sinusoidal model-based analysis and classification of stressed speech

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2018Speech emotion recognition research: an analysis of research focus 2015Cross-language acoustic emotion recognition: An overview and some tendencies 2021Speech emotion recognition based on multi‐feature and multi‐lingual fusion 2009Vocal emotion recognition in five native languages of Assam using new wavelet features

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