Ieee Automatic Speech Recognition And Understanding Workshop

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.
• Distinctive fingerprint for reviewer calibration: automatic, speech, recognition, spoken, language, understanding, low-resource, foundation, models, venue-specific, contribution, asru2025.
• 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

• What ASRU is. The IEEE biennial workshop on Automatic Speech Recognition and Understanding — focused, recognition-and-understanding-centered.
• vs Interspeech. Interspeech (ISCA) is the large general speech flagship; ASRU is a smaller, ASR-focused workshop.
• vs SLT. SLT is the sibling IEEE workshop on spoken language technologies (downstream tasks); ASRU centers recognition/understanding.

Method & evidence bar

• Use task-appropriate baselines, multiple datasets or languages when the claim is broad, and error analysis that explains model behavior.
• For LLM work, control for data leakage, prompt sensitivity, evaluation contamination, and human-evaluation reliability.
• For resources, document annotation, licensing, demographics, quality control, and intended use.
• For ASRU, the evidence must support the venue-specific signature: a speech-recognition paper with careful decoding, data, and robustness analysis.
• Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

Structure & house style

• State the language phenomenon, task, or system behavior before the model name.
• Connect examples to measured errors; reviewers dislike anecdotal examples presented as evidence.
• Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
• The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
• Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.

Official-cycle checklist

• Open the live official venue page: https://asru2025.org/
• Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
• Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
• Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
• If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

• One sentence states why this manuscript belongs at ASRU, using the venue's scope rather than generic "top conference" language.
• The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
• Related work includes the nearest current-cycle speech recognition papers and explains the technical delta.
• The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
• The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.

Common desk-reject triggers

• Evaluation that is only a prompt table or cherry-picked generation examples.
• Missing dataset documentation, licensing, or annotation reliability.
• Claims of general language understanding from narrow English-only benchmarks.
• Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
• A contribution framed for a neighboring field while giving ASRU reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses ASRU's bar, compare against annual-meeting-of-the-association-for-computational-linguistics / conference-on-empirical-methods-in-natural-language-processing / north-american-chapter-of-the-association-for-computational-linguistics / european-chapter-of-the-association-for-computational-linguistics. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/ieee-automatic-speech-recognition-and-understanding-workshop/SKILL.md