Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912388930863104 |
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| author | Kim, Heeseung Lee, Che Hyun Park, Sangkwon Yeom, Jiheum Park, Nohil Yu, Sangwon Yoon, Sungroh |
| author_facet | Kim, Heeseung Lee, Che Hyun Park, Sangkwon Yeom, Jiheum Park, Nohil Yu, Sangwon Yoon, Sungroh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models Kim, Heeseung Lee, Che Hyun Park, Sangkwon Yeom, Jiheum Park, Nohil Yu, Sangwon Yoon, Sungroh Sound Audio and Speech Processing 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. |
| title | Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.19759 |