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Self-assessed Emotion Classification from Acoustic and Physiological Features within Small-group Conversation.

, , and . ICMI Companion, page 230-239. ACM, (2021)

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Every Rating Matters: Joint Learning of Subjective Labels and Individual Annotators for Speech Emotion Classification., and . ICASSP, page 5886-5890. IEEE, (2019)Amplifying a Sense of Emotion toward Drama-Long Short-Term Memory Recurrent Neural Network for dynamic emotion recognition., , , , and . ROCLING, page 136-147. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2017)Do Minority Views in Perceptual Evaluations Affect Confidence of Speech Emotion Classifiers?. ACIIW, page 1-5. IEEE, (2022)Learning to Recognize Per-Rater's Emotion Perception Using Co-Rater Training Strategy with Soft and Hard Labels., and . INTERSPEECH, page 4108-4112. ISCA, (2020)Acoustic Indicators of Deception in Mandarin Daily Conversations Recorded from an Interactive Game., , , , and . INTERSPEECH, page 1731-1735. ISCA, (2019)Exploiting Annotators' Typed Description of Emotion Perception to Maximize Utilization of Ratings for Speech Emotion Recognition., , , and . ICASSP, page 7717-7721. IEEE, (2022)"Does it Matter When I Think You Are Lying?" Improving Deception Detection by Integrating Interlocutor's Judgements in Conversations., , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1846-1860. Association for Computational Linguistics, (2021)EMO-SUPERB: An In-depth Look at Speech Emotion Recognition., , , , , , , and . CoRR, (2024)Exploiting Co-occurrence Frequency of Emotions in Perceptual Evaluations To Train A Speech Emotion Classifier., , and . INTERSPEECH, page 161-165. ISCA, (2022)Joint Learning of Conversational Temporal Dynamics and Acoustic Features for Speech Deception Detection in Dialog Games., , and . APSIPA, page 1044-1050. IEEE, (2019)