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Hierarchical Tree Classification • Easily adaptable to other databases (AIBO databases) • Flexible framework • Exploit expert knowledge Chi-Chun Lee, Emily Mower, Carlos Busso, Sungbok Lee and Shrikanth S. Narayanan, Emotion recognition using a hierarchical binary decision tree approach (2011), in: Speech Communication, 53:9-10(1162-1171)
Emotion Recognition Context-Sensitive Multimodal • Considering temporal emotional context – When classifying the emotion of current observation • Hierarchical Framework to model dynamics (‏utterance – within emotions (e.g emotional (‏context – between emotions, during a conversation (temporal – between speakers • Flexibility in terms of classifiers (‏HMMs – HMM approaches (coupled HMMs, hierarchical (‏BLSTM‏)‏ – Reccurent Neural Networks and their extensions • Multimodal Fusion – Face, voice, head and hand movement cues
Page 1 and 2: Affective Computing : Computational
Page 3 and 4: Multimodality • Emotions are ofte
Page 5 and 6: Complex Representation of Emotion
Page 7 and 8: Motivation • Study of human emoti
Page 9 and 10: Emotion Representations Categorical
Page 11 and 12: Continuous Dimensional Representati
Page 13 and 14: Natural Language Descriptions EMO20
Page 15 and 16: A clip from CreativeIT database •
Page 17 and 18: Speech Production And Perception (G
Page 19 and 20: Speech Processing: Spectrogram (Rob
Page 21 and 22: ASR: HMM (http://en.wikipedia.org/w
Page 23 and 24: Facial Feature Extraction • Facia
Page 25 and 26: Examples of Features * Tracking Cha
Page 27: Emotion Recognition turn by turn re
Page 31 and 32: Tracking trends of continuous emoti
Page 33 and 34: Some Tracking Results • We are be
Page 35 and 36: Entrainment • Natural coordinatio

Rachel Study

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