# Re:Member: Emotional Question Generation from Personal Memories

arXiv: https://arxiv.org/abs/2510.19030 · HTML: https://mlsanity.com/paper/2510.19030/

## 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.

## Builds on (most cited first, 7 of 7)

- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [PCQPR: Proactive Conversational Question Planning with Reflection](https://mlsanity.com/paper/2410.01363/) (2024)
- [EDEN: Empathetic Dialogues for English learning](https://mlsanity.com/paper/2406.17982/) (2024)
- [Integrating emotional intelligence, memory architecture, and gestures to achieve empathetic humanoid robot interaction in an educational setting](https://mlsanity.com/paper/2505.19803/) (2025)
- [Comparative Evaluation of Expressive Japanese Character Text-to-Speech with VITS and Style-BERT-VITS2](https://mlsanity.com/paper/2505.17320/) (2025)
- [Animating Language Practice: Engagement with Stylized Conversational Agents in Japanese Learning](https://mlsanity.com/paper/2507.06483/) (2025)

## 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.