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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

classification cs.SDeess.AS
keywords modelsinteractionopen-sourcepastutterancesvoicerecallability
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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

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise

    cs.IR 2026-02 unverdicted novelty 7.0 of 10

    SQuTR is a large bilingual benchmark of 37,317 synthesized spoken queries under clean/low/medium/high noise, showing that retrieval quality steadily degrades as noise increases.

  2. A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A unified taxonomy and comparative meta-evaluation of automatic evaluation methods across text, vision, and speech generation, concluding that LLM-based evaluators dominate current practice.

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