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EMOTIONS RECOGNITION FROM SPEECH USING THE TRAINED MODEL
Pramod Mehra, Parag Jain, Shushil Kumar

Published in: International Journal of Current Engineering And Scientific Research ( IJCESR)
Volume- 4, Issue-10, pp.21-25, Oct 2017
DPI :-> 16.10046.IJCESR.2017.V4I10.2125.2206



Abstract
In this paper, the trained model is used to recognise the emotions from speech. As Speech has been used as an important mode of communication since the time immemorial. Emotions are an essential part of natural speech communication. Most of the present speech systems can process studio recorded neutral speech with greater accuracy. Therefore, a need is felt to update speech processing systems with the capability to process emotions. The component of emotion processing makes the existing speech systems more realistic and meaningful. In this work, spectral features are extracted from speech to perform emotion classification. Linear prediction cepstral coefficients, mel frequency cepstral coefficients and their derivatives (velocity and acceleration coefficients) are explored as features. Gaussian mixture models are proposed as classifiers. The emotions considered in this study are anger, happiness, neutral, sadness and surprise. The speech emotion database used in this work is semi-natural in nature, which has been collected from the dialogues of actors/actresses in popular Hindi movies.

Key-Words / Index Term
emotion classification, spectral features, GMM, MFCC, LPCC, text dependent emotion recognition, text independent emotion recognition

How to cite this article
Pramod Mehra, Parag Jain, Shushil Kumar , “EMOTIONS RECOGNITION FROM SPEECH USING THE TRAINED MODEL”, International Journal of Current Engineering And Scientific Research ( IJCESR), 4, Issue-10, pp.21-25, Oct 2017. DPI:16.10046.IJCESR.V4.I10.2206