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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 proposes epoch parameters extracted from LP (Linear Prediction) residual and zero frequency filtered speech signal for recognising the emotions present in speech. Instant of glottal closure within pitch period of LP residual is known as an 'epoch'. The significant excitation of vocal tract usually takes place at the instant of glottal closure. In this paper the epoch parameters namely strength...
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...
Spectral and excitation features, commonly used in automatic emotion classification systems, parameterise different aspects of the speech signal. This paper groups these features as speech production cues, broad spectral measures and detailed spectral measures and looks at how they differ in their performance in both speaker dependent and speaker independent systems. The extent of speaker normalisation...
The focus of this paper is on speech-based emotion detection utilising only acoustic data, i.e. without using any linguistic or semantic information. However, this approach in general suffers from the fact that acoustic data is speaker-dependent, and can result in inefficient estimation of the statistics modelled by classifiers such as hidden Markov models (HMMs) and Gaussian mixture models (GMMs)...
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