---
language:
- en
pretty_name: "SEAR: Spoofing Evidence-Grounded Audio Reasoning"
task_categories:
- audio-classification
- question-answering
tags:
- audio
- audio-deepfake-detection
- audio-language-model
- audio-question-answering
- acoustic-forensics
- asvspoof
- datasets
size_categories:
- 100K<n<1M
configs:
- config_name: t1
  default: true
  data_files:
  - split: 19LA_train
    path: t1/19LA_train.jsonl
  - split: 19LA_dev
    path: t1/19LA_dev.jsonl
  - split: 19LA_eval
    path: t1/19LA_eval.jsonl
  - split: 21LA_eval
    path: t1/21LA_eval.jsonl
- config_name: t2
  data_files:
  - split: 19LA_train
    path: t2/19LA_train.jsonl
  - split: 19LA_dev
    path: t2/19LA_dev.jsonl
  - split: 19LA_eval
    path: t2/19LA_eval.jsonl
  - split: 21LA_eval
    path: t2/21LA_eval.jsonl
- config_name: t3
  data_files:
  - split: 19LA_train
    path: t3/19LA_train.jsonl
  - split: 19LA_dev
    path: t3/19LA_dev.jsonl
  - split: 19LA_eval
    path: t3/19LA_eval.jsonl
  - split: 21LA_eval
    path: t3/21LA_eval.jsonl
- config_name: t4
  data_files:
  - split: 19LA_train
    path: t4/19LA_train.jsonl
  - split: 19LA_dev
    path: t4/19LA_dev.jsonl
  - split: 19LA_eval
    path: t4/19LA_eval.jsonl
  - split: 21LA_eval
    path: t4/21LA_eval.jsonl
---

# SEAR: Spoofing Evidence-Grounded Audio Reasoning

SEAR is an audio question-answering benchmark for testing whether audio language models
can identify and quantify signal-level acoustic anomalies and use them as evidence for
audio deepfake detection. Instead of evaluating only a final authenticity verdict, SEAR
separates deepfake detection, forgery-cue identification, acoustic measurement, and
forensic rationale generation.

SEAR contains four complementary tasks covering acoustic evidence identification and
quantification, forensic rationale grounding, and deepfake detection.

## Tasks

| Config | Task | Format | Evaluation target |
|---|---|---|---|
| `t1` | Binary classification | One multiple-choice question per audio | Deepfake detection |
| `t2` | Forgery cue identification | One multiple-choice question per audio | Acoustic evidence identification |
| `t3` | Acoustic feature measurement | Zero or more multiple-choice questions per audio | Acoustic evidence quantification |
| `t4` | Forensic rationale generation | One open-ended question per audio | Forensic rationale grounding |

T3 contains one question for each applicable reference finding. If no selected feature
is anomalous, the audio record is retained with an empty `qa_pairs` list and
`task_status.T3 = "not_applicable"`; no artificial measurement question is introduced.
T4 asks the model to summarize signal-level acoustic evidence in natural language. Its
reference rationale verbalizes the verified feature names, measured values, and deviation
directions without revealing the authenticity label or attack identity.

## Scale

| Source partition | Audio | T1 | T2 | T3 questions | T4 |
|---|---:|---:|---:|---:|---:|
| ASVspoof 2019 LA train | 25,380 | 25,380 | 25,380 | 96,318 | 25,380 |
| ASVspoof 2019 LA dev | 24,844 | 24,844 | 24,844 | 138,549 | 24,844 |
| ASVspoof 2019 LA eval | 71,237 | 71,237 | 71,237 | 639,204 | 71,237 |
| ASVspoof 2021 LA eval | 101,921 | 101,921 | 101,921 | 353,664 | 101,921 |
| **Total** | **223,382** | **223,382** | **223,382** | **1,227,735** | **223,382** |

The complete benchmark therefore contains 1,897,881 AQA items over 223,382 audio
recordings.

## Data format

Each JSONL row represents one audio recording. Questions for the same recording remain
grouped in `qa_pairs`, allowing an ALM runner to load the audio once and answer all
applicable questions.

```json
{
  "partition": "19LA_dev",
  "file_id": "LA_D_1000752",
  "audio_path": "data/LA/ASVspoof2019_LA_dev/flac/LA_D_1000752.flac",
  "label_offline": "spoof",
  "attack_metadata_offline": "A05",
  "qa_pairs": [
    {
      "qid": "LA_D_1000752_T2",
      "task": "forgery_cue_identification",
      "new_task": "T2",
      "question_type": "multiple_choice",
      "question": "Which acoustic finding best matches the deterministic analysis of this audio?",
      "options": {"A": "...", "B": "...", "C": "...", "D": "..."},
      "answer": "C",
      "answer_text": "...",
      "metadata": {"target_feature": "voiced_ratio", "no_anomaly_target": false}
    }
  ]
}
```

The paths are repository-relative identifiers for alignment with locally obtained source
audio. This repository does **not** redistribute ASVspoof audio.

## Loading

Replace `YOUR_NAMESPACE/SEAR` with this dataset repository's identifier:

```python
from datasets import load_dataset

t1 = load_dataset("YOUR_NAMESPACE/SEAR", "t1")
t2 = load_dataset("YOUR_NAMESPACE/SEAR", "t2")
t3 = load_dataset("YOUR_NAMESPACE/SEAR", "t3")
t4 = load_dataset("YOUR_NAMESPACE/SEAR", "t4")

example = t2["19LA_eval"][0]
print(example["audio_path"], example["qa_pairs"])
```

Users must obtain ASVspoof 2019 LA and ASVspoof 2021 LA from their official distribution
channels and resolve `audio_path` relative to their local copy.

## Evaluation protocol and leakage prevention

`label_offline`, `attack_metadata_offline`, `answer`, and `answer_text` are evaluation-side
fields. They must not be included in the model prompt. In particular:

- do not reveal authenticity labels or attack identities during inference;
- do not derive normalization statistics from development or evaluation data;
- use bona-fide training speech only when constructing a deployable reference prior;
- use development data only for model selection or threshold selection;
- reserve evaluation partitions for final reporting.

The included reference evidence is an offline benchmark target constructed with dataset
labels and available attack metadata. It is oracle supervision for evaluation, not the
output of a deployable detector.

## Construction and validation

SEAR uses 35 utterance-level descriptors spanning cepstral, spectral, prosodic, temporal,
and energy characteristics. Candidate evidence was statistically screened globally and,
when annotations were available, attack-wise. T2 presents a verified finding among
controlled distractors and a neutral no-anomaly option. T3 masks the measured value of a
specified finding and asks the model to recover it from four candidates.

All 223,382 audio records passed automated checks for partition and audio-path alignment,
task cardinality, answer mapping, option uniqueness, feature/value traceability, value
masking, and exclusion of attack identifiers from model-visible text. File counts and
SHA-256 checksums are provided in `manifest.json`.

For T4, references are generated solely from the verified structured findings and checked
for feature coverage, exact measured values, deviation directions, sentence count, and
forbidden class or attack inferences before release.

## Intended use and limitations

SEAR is intended for evaluating evidence-grounded acoustic reasoning in ALMs and for
controlled research on audio deepfake detection. The reference findings are statistical
descriptors rather than exhaustive causal explanations of spoofing. Performance may vary
under unseen recording conditions, languages, codecs, attacks, and demographic groups.
The source benchmarks and their collection processes may introduce additional biases.

## Source data and terms

This release provides AQA annotations and source-audio identifiers only. Users are
responsible for obtaining the corresponding ASVspoof data and complying with its terms of
use. No license is asserted here over the underlying audio recordings.

## Citation

The paper citation will be added upon publication. Until then, please cite the SEAR
dataset repository and the corresponding ASVspoof source benchmarks.