Stress and Emotion Classification using Jitter and Shimmer Features

Li, Xi; Jin, Tao; Johnson, Michael T.; Soltis, Joseph; Savage, Anne; Leong, Kirsten M.; Newman, J. D.

doi:10.1109/icassp.2007.367261

Cited by 108 publications

(54 citation statements)

References 7 publications

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“…They are commonly measured for long sustained vowels, and values of jitter and shimmer above a certain threshold are considered being related to pathological voices, which are usually perceived by humans as breathy, rough or hoarse voices. More recently, they have also been used to determine the classification of human speaking styles [36] and the age and gender of the speakers [11]. Absolute jitter values, for instance, are found larger in males, while…”

Section: Jitter and Shimmermentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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Jitter and shimmer are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. Both features have been largely used for the description of pathological voices, and since they characterise some aspects concerning particular voices, they are expected to have a certain degree of speaker specificity. In the current work, jitter and shimmer are successfully used in a speaker verification experiment. Moreover, both measures are combined with spectral and prosodic features using several types of normalisation and fusion techniques in order to obtain better verification results. The overall speaker verification system is also improved by using histogram equalisation as a normalisation technique previous to fusing the features by SVM.

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Section: Jitter and Shimmermentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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“…From literature and our experiments follows that different types of emotions are manifested not only in prosodic patterns (F0, energy, duration) and several voice quality features (e.g. jitter, shimmer, glottal-to-noise excitation ratio, Hammarberg index) (Li et al 2007) but also by significant changes in spectral domain (Nwe et al 2003). Several spectral features (spectral centroid, spectral flatness measure, Renyi entropy, etc.)…”

Section: Introductionmentioning

confidence: 99%

Spectral Properties and Prosodic Parameters of Emotional Speech in Czech and Slovak

Přibil¹,

Přibilová²

2011

Speech and Language Technologies

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“…On the other hand, Slyh et al (1999) and Li et al (2005) reported that significant differences can occur in jitter and shimmer measurements between different speaking styles, especially in shimmer measurement. Nevertheless, prosody is also highly-dependent on the emotion of the speaker, and prosodic features are useful in automatic recognition systems even when no emotional state is distinguished, which leads to the hypothesis that jitter and shimmer features can be also useful in the speaker recognition task.…”

Section: Jitter and Shimmermentioning

confidence: 99%

“…Jitter and shimmer, for example, have been largely used to detect pathological and characteristic voices like breathy, rough or hoarse voices (Michaelis et al, 1998;Kreiman and Gerrat, 2005). More recently, they have also been used to determine the age and gender of the speakers (Wittig and Müller, 2005) and the classification of human speaking styles (Li et al, 2005).…”

Section: Linguistic Levelsmentioning

confidence: 99%

Fusing prosodic and acoustic information for speaker recognition

Farrús

2009

The International Journal of Speech, Language and the Law

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ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author PhD Dissertation FUSING PROSODIC AND ACOUSTIC INFORMATION FOR SPEAKER RECOGNITION Mireia Farrús i Cabeceran AbstractAutomatic speaker recognition is the use of a machine to identify an individual from a spoken sentence. Recently, this technology has been undergone an increasing use in applications such as access control, transaction authentication, law enforcement, forensics, and system customisation, among others.One of the central questions addressed by this field is what is it in the speech signal that conveys speaker identity. Traditionally, automatic speaker recognition systems have relied mostly on short-term features related to the spectrum of the voice. However, human speaker recognition relies on other sources of information; therefore, there is reason to believe that these sources can play also an important role ...

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Stress and Emotion Classification using Jitter and Shimmer Features

Cited by 108 publications

References 7 publications

Using Jitter and Shimmer in speaker verification

Using Jitter and Shimmer in speaker verification

Spectral Properties and Prosodic Parameters of Emotional Speech in Czech and Slovak

Fusing prosodic and acoustic information for speaker recognition

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