Keep the Recording or Write It Down? A Matched-Budget Test of Audio Memory in Speech LLMs

A speech assistant that remembers past conversations can keep the old audio or write it down. We ask whether old audio is worth its cost when it competes with text for the same budget of context positions. On VoxMem 8K, MiniCPM-o 4.5 gains nothing measurable from old clips: spending an extra budget of a quarter of the audio's size on them changes accuracy by +0.7 points (95% interval −3.2 to +5.6; 300 question families). For this reader a bare transcript is within 10 points of the full audio history. Gemini 3.8 Flash behaves differently: it scores 18.8 points higher with audio than with a transcript. A text record with speaker, emotion and sound labels brings it to similar accuracy (43.8% against 41.7%), with a wide interval. Code, preregistrations, model outputs and verdicts: https://github.com/SeverinVisionary/voxmem-audio-vs-text-memory AI use: drafted with AI assistance (Claude), reviewed and approved by the author.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-27
DOI
https://doi.org/10.5281/zenodo.23682910
Primary Topic
Speech Recognition and Synthesis
Type
preprint
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preprint

Keep the Recording or Write It Down? A Matched-Budget Test of Audio Memory in Speech LLMs

Hanyu Yang
4 links
Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
preprint

Keep the Recording or Write It Down? A Matched-Budget Test of Audio Memory in Speech LLMs

Hanyu Yang
preprint en

Abstract

A speech assistant that remembers past conversations can keep the old audio or write it down. We ask whether old audio is worth its cost when it competes with text for the same budget of context positions. On VoxMem 8K, MiniCPM-o 4.5 gains nothing measurable from old clips: spending an extra budget of a quarter of the audio's size on them changes accuracy by +0.7 points (95% interval −3.2 to +5.6; 300 question families). For this reader a bare transcript is within 10 points of the full audio history. Gemini 3.8 Flash behaves differently: it scores 18.8 points higher with audio than with a transcript. A text record with speaker, emotion and sound labels brings it to similar accuracy (43.8% against 41.7%), with a wide interval. Code, preregistrations, model outputs and verdicts: https://github.com/SeverinVisionary/voxmem-audio-vs-text-memory AI use: drafted with AI assistance (Claude), reviewed and approved by the author.

Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
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