Comparison of Speaker Recognition by Training Emotion Specific GMM and Emotion Independent GMM

Volume 20 No 6 (2022)

Chandita Barman , Aditya Bihar Kandali , Franklin Burhagohain

DOI: 10.14704/nq.2022.20.6. NQ22206

Abstract

This paper is about the speaker recognition from the emotion speech samples. Data from ESDNEI is taken which consist of acted emotional speech samples of seven emotions: anger, disgust, fear, happy, neutral, sad and surprise by six speakers (3 female and 3 male) Assamese language. Using the data, we computed Mel-frequency cepstral coefficients (MFCC) as features and trained Gaussian Mixture Model (GMM) classifier for speaker recognition

Keywords

MFCC, GMM, EM Algorithm, Rabiner-Sambur algorithm

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