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Titlebook: Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction; COST Action 2102 Int Anna Esposito,Nikolaos G. Bourbakis,Ioanni

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發(fā)表于 2025-3-21 18:00:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
副標(biāo)題COST Action 2102 Int
編輯Anna Esposito,Nikolaos G. Bourbakis,Ioannis Hatzil
視頻videohttp://file.papertrans.cn/982/981106/981106.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction; COST Action 2102 Int Anna Esposito,Nikolaos G. Bourbakis,Ioanni
描述This book is dedicated to the dreamers, their dreams, and their perseverance in research work. This volume brings together the selected and peer–reviewed contributions of the p- ticipants at the COST 2102 International Conference on Verbal and Nonverbal F- tures of Human–Human and Human–Machine Interaction, held in Patras, Greece, October 29–31, 2007, hosted by the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008). The conference was sponsored by COST (European Cooperation in the Field of Scientific and Technical Research, www.cost.esf.org ) in the domain of Information and Communication Technologies (ICT) for disseminating the advances of the - search activity developed within COST Action 2102: “Cross-Modal Analysis of V- bal and Nonverbal Communication”(www.cost2102.eu). COST Action 2102 is a network of about 60 European and 6 overseas laboratories whose aim is to develop “an advanced acoustical, perceptual and psychological analysis of verbal and non-verbal communication signals originating in spontaneous face-to-face interaction, in order to identify algorithms and automatic procedures capable of identifying the human emotional states. Partic
出版日期Conference proceedings 2008
關(guān)鍵詞biometric; data mining; emotion recognition; facial expressions; facial patterns; gestures; hci; multimodal
版次1
doihttps://doi.org/10.1007/978-3-540-70872-8
isbn_softcover978-3-540-70871-1
isbn_ebook978-3-540-70872-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Ekfrasis: A Formal Language for Representing and Generating Sequences of Facial Patterns for Studyinsis) as a software methodology that synthesizes (or generates) automatically various facial expressions by appropriately combining facial features. The main objective here is to use this methodology to generate various combinations of facial expressions and study if these combinations efficiently represent emotional behavioral patterns.
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Study on Speaker-Independent Emotion Recognition from Speech on Real-World Data classifiers on utterance level is applied, in attempt to improve the performance of the emotion recognizer. Experimental results demonstrate significant differences on recognizing emotions on acted/real-world speech.
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Towards Slovak Broadcast News Automatic Recording and Transcribing Servicelso all automatically extracted metadata (verbal and nonverbal), and also to select incorrectly automatically identified data. The architecture of the present system is linear, which means every module starts after the previous has finished the data processing.
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Combining Features for Recognizing Emotional Facial Expressions in Static Images set was obtained combining PCA and LDA features (93% of correct recognition rate), whereas, combining PCA, LDA and Gabor filter features the net gave 94% of correct classification on facial expressions of subjects not included in the training set.
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Expressive Speech Synthesis Using Emotion-Specific Speech Inventoriesl for 99% of the logatoms and for all natural sentences. Recognition rates significantly above chance level were obtained for each emotion. The recognition rate for some synthetic sentences exceeded that of natural ones.
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