Re:Member: Emotional Question Generation from Personal Memories
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917071432974336 |
|---|---|
| author | Rackauckas, Zackary Minematsu, Nobuaki Hirschberg, Julia |
| author_facet | Rackauckas, Zackary Minematsu, Nobuaki Hirschberg, Julia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Re:Member: Emotional Question Generation from Personal Memories Rackauckas, Zackary Minematsu, Nobuaki Hirschberg, Julia Computation and Language Human-Computer Interaction 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. |
| title | Re:Member: Emotional Question Generation from Personal Memories |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.19030 |