NDLI: Hilbert–Huang–Hurst-based non-linear acoustic feature vector for emotion classification with stochastic models and learning systems

Content Provider	IET Digital Library
Author	Vieira, Vinícius Coelho, Rosângela Assis, Francisco Marcos de
Abstract	This study presents a widespread analysis of affective vocal expression classification systems. In this study, the Hilbert–Huang–Hurst coefficient (HHHC) vector is proposed as a non-linear vocal source feature to represent the emotional states according to their effects on the speech production mechanism. Affective states are highlighted by the empirical mode decomposition-based method, which exploits the non-stationarity of the acoustic variations. Hurst coefficients are then estimated from the decomposition modes to form the feature vector. Additionally, a vector of the index of non-stationarity (INS) is introduced as dynamic information to the HHHC. The proposed feature vector is evaluated in speech emotion classification experiments with three databases in German and English languages. Three state-of-the-art acoustic feature vectors are adopted as a baseline. The α -integrated Gaussian mixture model ( α -GMM) is also introduced for the emotion representation and classification. Its performance is compared to competing for stochastic and machine learning classifiers. Results demonstrate that the HHHC leads to significant classification improvement when compared to the baseline acoustic feature vectors. Moreover, results also show that the α -GMM outperforms the competing classification methods. Finally, the complementarity aspects of HHHC and INS are also evaluated for the GeMAPS and eGeMAPS feature sets.
Starting Page	522
Ending Page	532
Page Count	11
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 8, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0383
Journal	IET Signal Processing
Publisher Date	2020-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	$\alpha $α-GMM $\alpha $α-integrated Gaussian Mixture Model $α-GMM Acoustic Feature Vector Acoustic Signal Processing Affective Computing Affective Vocal Expression Classification System EGeMAPS Feature Set Emotion Recognition Emotion Representation Empirical Mode Decomposition English Language Ergonomic Aspects of Computing Gaussian Mixture Model Gaussian Processes GeMAPS Feature Set German Language HHHC Hilbert Transform Hilbert–Huang–Hurst Coefficient Vector Hilbert–Huang–Hurst-based Nonlinear Acoustic Feature Vector Index of Nonstationarity Integral Transforms Learning in AI Learning System Machine Learning Classifier Mixture Model Natural Language Processing Neural Computing Technique Nonlinear Vocal Source Feature Signal Classification Signal Representation Speech Emotion Classification Experiments Speech Enhancement Speech Processing Technique Speech Production Mechanism Speech Recognition Speech Recognition And Synthesis Statistics Stochastic Classifier Stochastic Linearised SCUC Stochastic Model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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