Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models
Heeseung Kim, Che Hyun Lee, Sangkwon Park, Jiheum Yeom, Nohil Park, Sangwon Yu, Sungroh Yoon
Abstract
Recent advancements in multi-turn voice interaction models have improved user-model communication. However, while closed-source models effectively retain and recall past utterances, whether open-source models share this ability remains unexplored. To fill this gap, we systematically evaluate how well open-source interaction models utilize past utterances using ContextDialog, a benchmark we proposed for this purpose. Our findings show that speech-based models have more difficulty than text-based ones, especially when recalling information conveyed in speech, and even with retrieval-augmented generation, models still struggle with questions about past utterances. These insights highlight key limitations in open-source models and suggest ways to improve memory retention and retrieval robustness.
BibTeX
@inproceedings{kim-etal-2025-voice-assistant,
title = "Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models",
author = "Kim, Heeseung and
Lee, Che Hyun and
Park, Sangkwon and
Yeom, Jiheum and
Park, Nohil and
Yu, Sangwon and
Yoon, Sungroh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.470/",
doi = "10.18653/v1/2025.findings-acl.470",
pages = "8984--9014",
ISBN = "979-8-89176-256-5"
}