Use when targeting IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for speech recognition.
SkillScore77Maintained
Automatic
◆Writing & Editing pack
Last verified 9/2/2026
What it changes
Passed 17 of 22 test cases with the skill, and 6 of 22 without it.
Without skill27%
With skill77%
Checks it passed that the model alone missed
Uses the standard 7-line bracketed metadata layout.
Formats the response using the bracketed field format.
Formats the evaluation into the bracketed line schema.
and 9 more in the full report
12 fixed5 passed either way4 still failing1 broke
Pass-rate lift
+50 pp
Tokens
+24%
Agent turns
0%
Response time
-41%
Model gemini-3.6-flashMeasured 9/2/2026v117 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
We evaluated an LLM-based speech foundation model on public speech recognition benchmarks and achieved state-of-the-art results. However, we did not perform data leakage checks or prompt sensitivity analysis against the pre-training text corpus. Diagnose this manuscript's readiness for ASRU and output the standard venue fit summary.
What was checked, without and with the skill
Checked
Without skill
With skill
Formats the response using the standard bracketed diagnostic fields.
Missed
Passed
Identifies the lack of evaluation for data contamination as a major evidence gap.
Passed
Passed
case-13 · gemini-3.6-flash · 9/2/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md99 linessingle file
IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Conference positioning
IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) is a top computer-science conference venue for automatic speech recognition, spoken language understanding, low-resource speech, and speech foundation models. It rewards a speech-recognition paper with careful decoding, data, and robustness analysis. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
The author names ASRU / IEEE Automatic Speech Recognition and Understanding Workshop as the target venue.
A manuscript in automatic speech recognition needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
Core fit: automatic speech recognition, spoken language understanding, low-resource speech, and speech foundation models.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to ASRU's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
Reviewer lens: Treat ASRU as a speech recognition venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: automatic speech recognition, spoken language understanding, low-resource speech, and speech foundation models.
Official anchor domain: asru2025.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Route to ASRU when the paper is centered on automatic speech recognition, speech
understanding, spoken-language modeling, or ASR evaluation.
Compare INTERSPEECH for broader speech processing, ICASSP for signal processing, SLT for
spoken-language technology, and ACL/EMNLP when language modeling is the core contribution.
What distinguishes this venue from its closest siblings
Use when targeting IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for signal processing.