paper-with-me

홈 › Papers

Comparison of Emotional Understanding in Modality-Controlled Environments using Multimodal Online Emotional Communication Corpus

2016-05-01 · LREC 2016 5 · Yoshiko Arimoto, Kazuo Okanoya

In online computer-mediated communication, speakers were considered to have experienced difficulties in catching their partner{'}s emotions and in conveying their own emotions. To explain why online emotional communication is so difficult and to investigate how this problem should be solved, multimodal online emotional communication corpus was constructed by recording approximately 100 speakers{'} emotional expressions and reactions in a modality-controlled environment. Speakers communicated over the Internet using video chat, voice chat or text chat; their face-to-face conversations were used for comparison purposes. The corpora incorporated emotional labels by evaluating the speaker{'}s dynamic emotional states and the measurements of the speaker{'}s facial expression, vocal expression and autonomic nervous system activity. For the initial study of this project, which used a large-scale emotional communication corpus, the accuracy of online emotional understanding was assessed to demonstrate the emotional labels evaluated by the speakers and to summarize the speaker{'}s answers on the questionnaire regarding the difference between an online chat and face-to-face conversations in which they actually participated. The results revealed that speakers have difficulty communicating their emotions in online communication environments, regardless of the type of communication modality and that inaccurate emotional understanding occurs more frequently in online computer-mediated communication than in face-to-face communication.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Emotion Recognition in Context

2017-07-01 · CVPR 2017 7 · Ronak Kosti, Jose M. Alvarez, Adria Recasens, Agata Lapedriza

Understanding what a person is experiencing from her frame of reference is essential in our everyday life. For this reason, one can think that machines with this type of ability would interact better with people. However…

Emotion RecognitionEmotion Recognition in Context

Learning What to Attend First: Modality-Importance-Guided Reasoning for Reliable Multimodal Emotion Understanding

2025-12-02 · Hyeongseop Rha, Jeong Hun Yeo, Junil Won, Se Jin Park 외 arxiv

In this paper, we present Modality-Importance-Guided Reasoning (MIGR), a framework designed to improve the reliability of reasoning-based multimodal emotion understanding in multimodal large language models. Although exi…

Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction

2023-11-08 · Cam-Van Thi Nguyen, Anh-Tuan Mai, The-Son Le, Hai-Dang Kieu 외

Emotion recognition is a crucial task for human conversation understanding. It becomes more challenging with the notion of multimodal data, e.g., language, voice, and facial expressions. As a typical solution, the global…

Emotion RecognitionGraph Neural NetworkMultimodal Emotion Recognition

Edu-EmotionNet: Cross-Modality Attention Alignment with Temporal Feedback Loops

2025-10-09 · S M Rafiuddin arxiv

Understanding learner emotions in online education is critical for improving engagement and personalized instruction. While prior work in emotion recognition has explored multimodal fusion and temporal modeling, existing…

Emotion Recognition

Retrieval of multimedia stimuli with semantic and emotional cues: Suggestions from a controlled study

2017-06-30 · Horvat Marko, Kukolja Davor, Ivanec Dragutin

The ability to efficiently search pictures with annotated semantics and emotion is an important problem for Human-Computer Interaction with considerable interdisciplinary significance. Accuracy and speed of the multimedi…

Retrieval