Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

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 speechbased models have more difficulty than textbased 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 opensource models and suggest ways to improve memory retention and retrieval robustness.

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.470
Primary Topic
Speech and dialogue systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

Nohil Park, Heeseung Kim, Sungroh Yoon, Sangkwon Park et al.
Speech and dialogue systems
article

Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

Nohil Park, Heeseung Kim, Sungroh Yoon, Sangkwon Park, Jiheum Yeom, Shyr-Shen Yu, Che Hyun Lee
article en

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 speechbased models have more difficulty than textbased 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 opensource models and suggest ways to improve memory retention and retrieval robustness.

National Research Foundation, Seoul National University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Samsung, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 90%
Speech and dialogue systems
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Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models — Nohil Park, Heeseung Kim, et al. · (2025) | TGRS Research Map | TGRS