The reviewed record of science sign in
Pith

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

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:IQSJEGLVrecord.jsonopen to challenge →

classification cs.SD eess.AS
keywords modelsinteractionopen-sourcepastutterancesvoicerecallability
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

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

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

    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.