Re:Member: Emotional Question Generation from Personal Memories
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Abstract (arXiv)
We present Re:Member, a system that explores how emotionally expressive, memory-grounded interaction can support more engaging second language (L2) learning. By drawing on users' personal videos and generating stylized spoken questions in the target language, Re:Member is designed to encourage affective recall and conversational engagement. The system aligns emotional tone with visual context, using expressive speech styles such as whispers or late-night tones to evoke specific moods. It combines WhisperX-based transcript alignment, 3-frame visual sampling, and Style-BERT-VITS2 for emotional synthesis within a modular generation pipeline. Designed as a stylized interaction probe, Re:Member highlights the role of affect and personal media in learner-centered educational technologies.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 21 Oct 2025.
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7 of 17 references.
References · 7
- 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
- 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
- 2024PCQPR: Proactive Conversational Question Planning with Reflectioncited by 6
- 2024EDEN: Empathetic Dialogues for English learningcited by 5
- 2025Integrating emotional intelligence, memory architecture, and gestures to achieve empathetic humanoid robot interaction in an educational settingcited by 2
- 2025Comparative Evaluation of Expressive Japanese Character Text-to-Speech with VITS and Style-BERT-VITS2cited by 1
- 2025Animating Language Practice: Engagement with Stylized Conversational Agents in Japanese Learningcited by 1
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