Emotion Recognition for Affective User Interfaces using Natural Language Dialogs

Lee, Cheongjae; Lee, G.G.

doi:10.1109/roman.2007.4415194

Cited by 11 publications

(9 citation statements)

References 2 publications

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“…Speech has been used in conjunction with other modalities such as text Lee and Lee (2007) Bellegarda (2013) Goyal et al (2010), body gestures, and facial expressions to build multi-modal models for emotion classificationÖzkul et al (2012) Wu et al (2013) Huisman et al (2013), but in this paper we focus on emotion classification based solely on vocal features.…”

Section: Introductionmentioning

confidence: 99%

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Yang

Yuan

Zhou

et al. 2016

Int J Speech Technol

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As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-againstall support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three stateof-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified.We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.

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Section: Introductionmentioning

confidence: 99%

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Yang

Yuan

Zhou

et al. 2016

Int J Speech Technol

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show abstract

“…One of the major drawback of this system is lack of contextual information extraction. Generally, keyword spotting is very popular and naïve technique to extract emotions based on emotional keywords existence in the text [18]. If there is no existence of emotional keywords then this technique fails to articulate emotion from text [19].…”

Section: B Abstractpotting Techniquementioning

confidence: 99%

Context based Emotion Analyzer for Interactive Agent

Malik¹,

Raza²,

Asif³

2017

ijacsa

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“…Linguistic, pragmatic, and keyword spotting features model (C. Lee & G. Lee, 2007) accuracy result is based on a corpus consists of 2900 items from in 10 domains including "sports", "love", "weather", "music" and others. 2122 items are natural items and 778 items are emotional item.…”

Section: Proposed Model Versus Binary Classifier Modelsmentioning

confidence: 99%

Frame Semantics Evolutionary Model for Emotion Detection

Haggag¹

2014

CIS

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Emotions play a significant role in identifying attitude, state, condition or mode of a particular circumstance. Textual data, in particular, involves emotional state and affective communication beside its informative contents. Emotion extraction from text has been potentially studied to stimulate and elicit articulation features. In this study, a machine learning emotion detection model is proposed for textual emotion recognition. A frame semantics approach is identified to extract knowledge from the text in an evolutionary process that improves the detection capabilities. Emotion detection process is controlled by a rule base; each of its entries is generated by pre-invoking event, action and resulting emotion state. Frame entities semantically collaborated to evaluate the frame emotion. Individual entities may arbitrary substituted by their synonyms or opposites if a candidate frame doesn't match any of the knowledge set. The proposed model proves considerable capability of recognizing emotions by referencing their semantic relations. Results showed better detection accuracy for the proposed model compared with variety of emotion approaches including keyword spotting, knowledge-based ANN and supervised machine learning models. Experiments indicated encouraging results over both binary emotion and multiple labels classifiers.

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Emotion Recognition for Affective User Interfaces using Natural Language Dialogs

Cited by 11 publications

References 2 publications

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Context based Emotion Analyzer for Interactive Agent

Frame Semantics Evolutionary Model for Emotion Detection

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