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

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arxiv 2502.19759 v2 pith:IQSJEGLV submitted 2025-02-27 cs.SD eess.AS

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

classification cs.SD eess.AS
keywords modelsinteractionopen-sourcepastutterancesvoicerecallability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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    cs.IR 2026-02 unverdicted novelty 7.0

    SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme ac...

  2. Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey

    eess.AS 2025-05 accept novelty 6.0

    The survey introduces a four-category taxonomy for LALM evaluations and reviews benchmarks across general auditory processing, knowledge reasoning, dialogue, and fairness-safety.