Stress and emotion classification using jitter and shimmer features

Li Xi; Tao Jidong; Michael T. Johnson; Joseph Solds; Anne Savage; Kirsten M. Leong; John D. Newman

doi:10.1109/ICASSP.2007.367261

Stress and emotion classification using jitter and shimmer features

Li Xi, Tao Jidong, Michael T. Johnson, Joseph Solds, Anne Savage, Kirsten M. Leong, John D. Newman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

61 Scopus citations

Abstract

In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency Cepstral Coefficients (MFCCs) for human speech and Greenwood Function Cepstral Coefficients (GFCCs) for animal vocalizations. Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking styles as well as vocalizations labeled by arousal level for African Elephant and Rhesus Monkey species

Original language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
DOIs	https://doi.org/10.1109/ICASSP.2007.367261
State	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: Apr 15 2007 → Apr 20 2007

Other

Other	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	4/15/07 → 4/20/07

Keywords

GFCC
HMM
Jitter
MFCC
Shimmer

ASJC Scopus subject areas

Electrical and Electronic Engineering
Signal Processing
Acoustics and Ultrasonics

Access to Document

10.1109/ICASSP.2007.367261

Cite this

Xi, L, Jidong, T, Johnson, MT, Solds, J, Savage, A, Leong, KM & Newman, JD 2007, Stress and emotion classification using jitter and shimmer features. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 4, 4218292, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 4/15/07. https://doi.org/10.1109/ICASSP.2007.367261

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abstract = "In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency Cepstral Coefficients (MFCCs) for human speech and Greenwood Function Cepstral Coefficients (GFCCs) for animal vocalizations. Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking styles as well as vocalizations labeled by arousal level for African Elephant and Rhesus Monkey species",

keywords = "GFCC, HMM, Jitter, MFCC, Shimmer",

author = "Li Xi and Tao Jidong and Johnson, {Michael T.} and Joseph Solds and Anne Savage and Leong, {Kirsten M.} and Newman, {John D.}",

year = "2007",

doi = "10.1109/ICASSP.2007.367261",

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AU - Xi, Li

AU - Jidong, Tao

AU - Johnson, Michael T.

AU - Solds, Joseph

AU - Savage, Anne

AU - Leong, Kirsten M.

AU - Newman, John D.

PY - 2007

Y1 - 2007

N2 - In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency Cepstral Coefficients (MFCCs) for human speech and Greenwood Function Cepstral Coefficients (GFCCs) for animal vocalizations. Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking styles as well as vocalizations labeled by arousal level for African Elephant and Rhesus Monkey species

AB - In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency Cepstral Coefficients (MFCCs) for human speech and Greenwood Function Cepstral Coefficients (GFCCs) for animal vocalizations. Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking styles as well as vocalizations labeled by arousal level for African Elephant and Rhesus Monkey species

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KW - HMM

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BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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Y2 - 15 April 2007 through 20 April 2007

ER -

Stress and emotion classification using jitter and shimmer features

Abstract

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Keywords

ASJC Scopus subject areas

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