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

Spring

書目名稱Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction影響因子(影響力)




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書目名稱Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction被引頻次




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書目名稱Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction讀者反饋




書目名稱Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction讀者反饋學(xué)科排名

發(fā)酵劑

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.

commensurate

Presbycusis

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.

onlooker

MORT

巧辦法

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.

Gyrate

TEM

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.

Bombast

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.