A Weight Based Approach for Emotion Recognition from Speech: An Analysis Using South Indian Languages

Poorna, S. S.; Anuraj, K; Nair, G. J.

doi:10.1007/978-981-13-1936-5_2

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Basic Frmework Of Speech Emotion Recognition (Ser)1

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2019

2023

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Cited by 5 publications

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“…Normally choosing of a particular classifier is based on experimental results or thumb rule. Generally classifiers are categorized in linear classifiers (Naive Bayes classifier) and nonlinear classifiers (GMM, HMM) [16,17].…”

Section: Basic Frmework Of Speech Emotion Recognition (Ser)mentioning

confidence: 99%

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

Tank

Hadia

2020

IJECE

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

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Section: Basic Frmework Of Speech Emotion Recognition (Ser)mentioning

confidence: 99%