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Telugu based Emotion Recognition System using Hybrid Features

by J. Naga Padmaja, R. Rajeswara Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 37
Year of Publication: 2019
Authors: J. Naga Padmaja, R. Rajeswara Rao
10.5120/ijca2019918359

J. Naga Padmaja, R. Rajeswara Rao . Telugu based Emotion Recognition System using Hybrid Features. International Journal of Computer Applications. 182, 37 ( Jan 2019), 9-16. DOI=10.5120/ijca2019918359

@article{ 10.5120/ijca2019918359,
author = { J. Naga Padmaja, R. Rajeswara Rao },
title = { Telugu based Emotion Recognition System using Hybrid Features },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 182 },
number = { 37 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number37/30304-2019918359/ },
doi = { 10.5120/ijca2019918359 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:32.954974+05:30
%A J. Naga Padmaja
%A R. Rajeswara Rao
%T Telugu based Emotion Recognition System using Hybrid Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 37
%P 9-16
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emotion recognition from speech is experiencing different research applications. It is becoming one of the tool for analysis of health condition of the speaker. In this work, the emotions such as anger, fear, happy, neutral are considered for speech emotion algorithm design. A database built by IITKGP is used for emotion recognition. For any recognition, feature extraction and pattern classification are the important tasks. In this work the features considered are Mel Frequency Cepstral Coefficients (MFCC), Pitch chroma, prosodic are used. Hidden Markov Models (HMMs ) are used to for modeling and identify the emotions. In this research work, the database considered for emotion recognition is taken in different combinations such as male training- female testing, male training-male testing, female training- female testing, female training-male testing. All these combinations are trained and tested with i-vector with GMM, linear Hidden Markov Models (HMMs) and Ergodic Hidden Markov Models(EHMMs) In almost all the cases, Ergodic Hidden Markov Models (EHMMs) method has shown significant improvement in recognition accuracy than i-vector with GMM and Linear Hidden Markov Models(HMMs)

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Index Terms

Computer Science
Information Sciences

Keywords

Emotion Specific I-Vector Gaussian Mixture Models Prosody Features Spectral Features HMM EHMM.

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