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In this paper, prosodic analysis of speech segments is performed to recognise emotions. Speech signal is segmented into words and syllables. Energy and pitch parameters are extracted from utterances, words and syllables separately to develop emotion recognition models. Eight emotions (anger, disgust, fear, happy, neutral, sad, sarcastic and surprise) of simulated emotion speech corpus, IITKGP SESC...
In this work, vowel onset points (VOPs) and pitch based spectral features are used for speech emotion classification. VOP is an anchor point from which vowel begins in a CV unit (generally a syllable). These are estimated using energy values of linear prediction (LP) residual, short time spectrum and modulation spectrum. Identification of vowel, consonant and CV transition regions of a syllable is...
This paper explores the Linear Prediction (LP) residual of speech signal for characterizing the basic emotions. The emotions used in this study are anger, compassion, disgust, fear, happy, neutral, sarcastic and surprise. LP residual is derived by inverse filtering of the speech signal, and the process is known as LP analysis. LP residual mainly contains higher order relations among the samples. For...
This paper aims at exploring short term spectral features for Emotion Recognition (ER). Linear predictive cepstral coefficients (LPCC), mel frequency cepstral coefficients (MFCC) and log frequency power co-efficients (LFPC) are explored for classification of emotions. For capturing the emotion specific knowledge from the above short-term speech features vector quantizer (VQ) models are used in this...
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