Text Independent Emotion Recognition Using Spectral Features
This paper presents text independent emotion recognition from speech using mel frequency cepstral coefficients (MFCCs) along with their velocity and acceleration coefficients. In this work simulated Hindi emotion speech corpus, IITKGP-SEHSC is used for conducting the emotion recognition studies. The emotions considered are anger, disgust, fear, happy, neutral, sad, sarcastic, and surprise. Gaussian mixture models are used for developing emotion recognition models. Emotion recognition performance for text independent and text dependent cases are compared. Around 72% and 82% of emotion recognition rate is observed for text independent and dependent cases respectively.
KeywordsGaussian mixture models emotion recognition IITKGP-SEHSC spectral features text dependent emotion recognition text independent emotion recognition
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