# ICASSP 2026 review form

> Simulate ICASSP-style reviews: confidence, Importance/Relevance, Novelty, Technical Correctness, Experimental Validation, Clarity and References on 1–4, the four-level Overall Evaluation (Definite reject … Definite accept) and a detailed assessment for the authors.

- Venue: IEEE International Conference on Acoustics, Speech and Signal Processing
- Form status: modeled, not official. ICASSP does not publish its review form. The criteria and their order come from the official ICASSP 2025 reviewer instructions and the 2026/2027 review criteria; numbered labels are from a public ICASSP 2023 review where seen, and the remaining levels (and the paper-type and confidence options) are ours.
- Official source: https://2025.ieeeicassp.org/editorial-procedures/
- Overall score: Overall Evaluation Of This Paper, 1–4
- Rough acceptance bar (our simulation): mean ≥ 2.75
- Web page: https://reviewpaper.ai/venues/icassp

## How reviewers read the paper

ICASSP papers get at least three reviews on a fixed list of criteria (importance, novelty, technical correctness, experimental validation, clarity, references), an overall evaluation and a detailed assessment shared with the authors. Extreme ratings carry meaning: "Irrelevant", "Has been done before", "Has major problems", "Insufficient validation" and "Does not cite relevant work" each imply rejection and must be justified. The optional rebuttal goes to area and track chairs, not the reviewers. Review is single-anonymous (authors are visible to reviewers).

- Write "the paper", not "you" or "the authors"; no sarcasm.
- Scores must match the comments; use the whole scale.
- "Done before" or "well known" must come with references.
- Ignore minor formatting problems; be specific about writing issues.
- Do not ask the authors to cite your own work unless directly relevant.

**Where the bar sits.** ICASSP 2026 accepted close to 4,800 of ~11,200 regular submissions (~43%). TPC chairs set the rate; no thresholds are published. On the 1–4 overall scale a mean of ≥ 2.75 (mostly Marginal accept or better) is a rough proxy for acceptance.

## Reviewer form, field by field

### 1. Classify The Type Of Paper

Type: single choice.

The amount of experimental validation expected depends on the paper type.

- Theoretical
- Algorithmic / methodological
- Experimental / application
- Dataset / resource
- Survey / overview

### 2. Importance/Relevance

Type: score, 1–4.

"Of limited interest" only if novelty, clarity and correctness are excellent; "Irrelevant" implies rejection and must be justified thoroughly.

- **4**: Of broad interest
- **3**: Of sufficient interest
- **2**: Of limited interest
- **1**: Irrelevant

### 3. Originality/Novelty

Type: score, 1–4.

"Very original" opens new directions. "Has been done before" implies rejection and requires references.

- **4**: Very original
- **3**: Moderately original
- **2**: Minor originality
- **1**: Has been done before

### 4. Technical Correctness / Theoretical Development

Type: score, 1–4.

List any errors found. "Has major problems" implies rejection.

- **4**: Definitely correct
- **3**: Probably correct
- **2**: Has minor errors
- **1**: Has major problems

### 5. Experimental Validation

Type: score, 1–4.

Judge against the paper type. "Insufficient validation" implies rejection.

- **4**: Sufficient validation / theoretical paper
- **3**: Limited but convincing
- **2**: Lacking in some respects
- **1**: Insufficient validation

### 6. Clarity Of Presentation

Type: score, 1–4.

Be specific about writing problems; ignore minor formatting issues.

- **4**: Very clear
- **3**: Clear enough
- **2**: Difficult to understand in places
- **1**: Very difficult to understand

### 7. Reference To Prior Work

Type: score, 1–4.

"References missing": list them. "Does not cite relevant work" implies rejection; use only when the missing work is well known.

- **4**: Excellent references
- **3**: References adequate
- **2**: References missing
- **1**: Does not cite relevant work

### 8. Overall Evaluation Of This Paper

Type: score, 1–4.

Use the whole scale; marginal scores only when the paper really is in the middle. A 1 or 2 must be justified.

- **4**: Definite accept
- **3**: Marginal accept
- **2**: Marginal reject
- **1**: Definite reject

### 9. Award Quality

Type: single choice.

Should the paper be considered for a best-paper award?

- No
- Yes

### 10. Strengths

Type: list of points. Part of "Detailed Assessment Of The Paper".

Specific strengths, consistent with the scores above.

### 11. Weaknesses

Type: list of points. Part of "Detailed Assessment Of The Paper".

Specific weaknesses and how to fix them; every low score must be explained here. Cite references for any "well known" claim.

### 12. Confidence

Type: score, 1–4.

How confident you are in your evaluation of this paper.

- **4**: Expert in the area
- **3**: Knowledgeable
- **2**: Some familiarity
- **1**: Not familiar with the area

## Area chair meta-review

ICASSP meta-reviewers and area chairs recommend to track chairs; TPC chairs give final approval.

Recommendation options: Accept · Reject

## ICASSP submission requirements

1. 4 pages of content plus an optional 5th page for references only
2. Generative-AI content disclosed in the acknowledgments (system and sections); grammar/editing use exempt
3. At most 9 submissions per author

## LLM policy

IEEE policy forbids processing any content of a manuscript under review through a public AI platform (a confidentiality breach). ICASSP 2026 organizers report using the rebuttal phase to identify AI-generated reviews.

Official policy: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/submission-and-peer-review-policies/

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Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=icassp (3 free reviews). Sample report: https://reviewpaper.ai/sample.
reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. Not affiliated with any conference.