Investigating glottal parameters and teager energy operators in emotion recognition

Authors:
Rui Sun;Elliot Moore
Affiliations:
Georgia Institute of Technology, School of Electrical and Computer Engineering, Savannah, GA;Georgia Institute of Technology, School of Electrical and Computer Engineering, Savannah, GA
Venue:
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Year:
2011

Citing 14
Cited 0

2005 Special Issue: Emotion recognition in human-computer interaction

Neural Networks - Special issue: Emotion and brain
A performance assessment of objective measures for evaluating the quality of glottal waveform estimates

Speech Communication
Investigating glottal parameters for differentiating emotional categories with similar prosodics

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Opensmile: the munich versatile and fast open-source audio feature extractor

Proceedings of the international conference on Multimedia
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

IEEE Transactions on Affective Computing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels

IEEE Transactions on Affective Computing
Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space

IEEE Transactions on Affective Computing
Interdependencies among Voice Source Parameters in Emotional Speech

IEEE Transactions on Affective Computing
AVEC 2011-the first international audio/visual emotion challenge

ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Affective computing: a review

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Estimation of Glottal Closure Instants in Voiced Speech Using the DYPSA Algorithm

IEEE Transactions on Audio, Speech, and Language Processing
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The purpose of this paper is to study the performance of glottal waveform parameters and TEO in distinguishing binary classes of four emotion dimensions (activation, expectation, power, and valence) using authentic emotional speech. The two feature sets were compared with a 1941-dimension acoustic feature set including prosodic, spectral, and other voicing related features extracted using openSMILE toolkit. The comparison work highlight the discrimination ability of TEO in emotion dimensions activation and power, and glottal parameters in expectation and valence for authentic speech data. Using the same classification methodology, TEO and glottal parameter outperformed or performed similarly to the prosodic, spectral and other voicing related features (i.e., the feature set obtained using openSMILE).