Speech Emotion Recognition Based on Rough Set and SVM

Zhou, Jian; Wang, Guoyin; Yang, Yong; Pei-jun, Chen

doi:10.1109/coginf.2006.365676

Cited by 52 publications

(18 citation statements)

References 6 publications

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“…Zhou et al, use an approach based on rough Set theory and SVM for speech emotion recognition. The experiment results show that this approach can reduce the calculation cost while keeping high recognition rate [6].…”

Section: Related Workmentioning

confidence: 82%

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A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

Machot

Mosa

Fasih

et al. 2012

Autonomous Systems: Developments and Trends

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This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.

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Section: Related Workmentioning

confidence: 82%

“…In the recent results of speech emotion recognition systems, researchers in [6] use 37 features of the voice streams. They classify 6 types of emotions, their total accuracy was 74% based on a combination of support vector machine (SVM) and Rough Set theory and 77,91% based on only SVM.…”

Section: Resultsmentioning

confidence: 99%

A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

Machot

Mosa

Fasih

et al. 2012

Autonomous Systems: Developments and Trends

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“…More detailed information can be found in (Ververidis & Kotropoulos, 2006). However, even though many researches have been carried out to find acoustic features suitable for emotion recognition, there is still no conclusive evidence to show which set of features can provide the best recognition accuracy (Zhou, 2006). After the acoustic features are extracted and processed, they are sent to emotion classification module.…”

Section: Related Workmentioning

confidence: 99%

“…As there is a lack of precise definition and models for emotions, automatic recognition of emotions has been a challenging task to researchers. Indeed, research on speech based emotion recognition has been undertaken by many for around two decades (Amir, 2001;Clavel et al, 2004;Cowie & Douglas-Cowie, 1996;Cowie et al, 2001;Dellaert et al, 1996;Lee & Narayanan, 2005;Morrison et al, 2007;Nguyen & Bass, 2005;Nicholson et al, 1999;Petrushin, 1999;Petrushin, 2000;Scherer, 2000;Ser et al, 2008;Ververidis & Kotropoulos, 2006;Yu et al, 2001;Zhou et al, 2006). In engineering, speech emotion recognition has been formulated as a pattern recognition problem that involves feature extraction and emotion classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Emotional States From Human Speeches

Cen¹,

Ser²,

Yu³

et al. 2010

Pattern Recognition Recent Advances

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“…Focusing on these problems, many successful works have been addressed and discussed. For example, Zhou et al [40] presented a novel approach based on rough set theory and SVM for speech emotion recognition. The experiment results illustrated that the introduced approach can reduce the calculation cost while keeping high recognition rate.…”

Section: In Speech Emotion Recognitionmentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

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Speech Emotion Recognition Based on Rough Set and SVM

Cited by 52 publications

References 6 publications

A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

Automatic Recognition of Emotional States From Human Speeches

Computational Intelligence in Multimedia Processing: Foundation and Trends

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