| --- |
| license: other |
| license_name: usc-research |
| license_link: LICENSE |
| language: |
| - en |
| pretty_name: VoxParadox |
| task_categories: |
| - audio-classification |
| - question-answering |
| tags: |
| - audio |
| - speech |
| - paralinguistic |
| - benchmark |
| - adversarial |
| - audio-llm |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: "audio/*.wav" |
| --- |
| |
| <div align="center"> |
|
|
| <img src="assets/voxparadox_logo_full.png" alt="VoxParadox" width="360"> |
|
|
| **Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox** |
|
|
| Jiacheng Pang<sup>\*</sup>, Ashutosh Chaubey<sup>\*</sup>, Mohammad Soleymani \ |
| University of Southern California \ |
| <sub><sup>*</sup> Equal contribution</sub> |
| |
| [](https://icml.cc/Conferences/2026) |
| [](https://arxiv.org/abs/2605.27772) |
| [](https://voxparadox.github.io/) |
| [](https://github.com/ihp-lab/VoxParadox) |
| [](#) |
| [](LICENSE) |
| |
| </div> |
| |
| --- |
| |
| An adversarial speech QA benchmark for **paralinguistic understanding** in |
| Audio LLMs. Each example is built around a controlled linguistic–acoustic |
| contradiction: the transcript explicitly asserts an incorrect paralinguistic |
| attribute, while the audio reliably conveys the correct one. Models that |
| defer to transcript content are misled; models that listen are not. |
| |
| **2,000 MCQs** across **10 paralinguistic tasks** (200 each). |
| |
| ## Quick start |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("IHP-Lab/VoxParadox", split="test") |
| print(ds[0]) # includes `audio` (decoded), `question`, `choice_a..d`, |
| # `answer_gt`, `adversarial_labels`, `task_name`, `id` |
| ``` |
| |
| ## Tasks |
| |
| `y_true` (audio) and `y_adv` (transcript) are disjoint by construction. |
| |
| | Task (`task_name`) | Acoustic attribute the model must recover | |
| |---|---| |
| | `age_prediction` | Speaker's age group | |
| | `gender_prediction` | Speaker's gender | |
| | `emotion_recognition` | Emotion conveyed by delivery (high-contrast pairs) | |
| | `intonation_perception` | Rising vs. falling intonation | |
| | `speaker_identity_recognition` | Which segment shares a speaker with a queried segment | |
| | `total_speaker_counting` | Number of distinct speakers | |
| | `pitch_comparison` | Ordering of three segments by pitch | |
| | `volume_comparison` | Ordering of three segments by loudness | |
| | `speed_comparison` | Ordering of three segments by speaking rate | |
| | `vocal_range_comparison` | Ordering of three segments by pitch range | |
| |
| ## File layout |
| |
| ``` |
| . |
| ├── metadata.jsonl # one record per example (loaded by `datasets`) |
| ├── voxparadox.json # same content as JSON array (for direct inspection) |
| ├── audio/ # 2,000 wav files |
| ├── eval.py # evaluation script (GT accuracy + ALA) |
| ├── assets/ # logo |
| └── LICENSE # USC Research License |
| ``` |
| |
| ## Record schema |
| |
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | `{task_name}__{N}`, with `N` running 0–199 within each task. | |
| | `task_name` | string | One of the 10 tasks above. | |
| | `file_name` / `audio_path` | string | Path to the audio clip, relative to this directory. | |
| | `question` | string | The MCQ question prompt. | |
| | `choice_a` / `choice_b` / `choice_c` / `choice_d` | string | The four answer options. | |
| | `answer_gt` | string | The acoustic ground-truth `y_true` (one of the four choices). | |
| | `adversarial_labels` | list[string] | The transcript-implied label(s) `y_adv`. Single-element for most tasks; 2 elements for the four `*_comparison` tasks. | |
| |
| ## Evaluation |
| |
| Two complementary metrics: |
| |
| * **GT Accuracy** — fraction matching `answer_gt`. Higher is better; reflects use of acoustic evidence. |
| * **Adversarial-Label Agreement (ALA)** — fraction matching any string in `adversarial_labels`. Higher ALA means more transcript-following. |
| |
| Run `eval.py` on a JSONL of model predictions (one record per line, fields `id` and `response`): |
| |
| ```bash |
| python eval.py --predictions preds.jsonl |
| ``` |
| |
| Example prediction record: |
| ```json |
| {"id": "age_prediction__0", "response": "(C) Elderly adult."} |
| ``` |
| |
| The script parses A/B/C/D from the response (letter-first, then choice-text |
| fallback), prints per-task and overall GT/ALA, and optionally writes a JSON |
| report with `--report report.json`. |
| |
| ## License |
| |
| Released under the **USC Research License** (research and non-profit use only; |
| commercial use requires a separate license). See [`LICENSE`](LICENSE) for the |
| full text. |
| |
| Audio was synthesized via commercial TTS engines (ElevenLabs, GPT-4o, |
| Microsoft Azure); any commercial reuse of the audio is additionally subject |
| to those vendors' terms of service. |
| |
| ## Citation |
| ```bibtex |
| @inproceedings{pang2026voxparadox, |
| title = {Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox}, |
| author = {Pang, Jiacheng and Chaubey, Ashutosh and Soleymani, Mohammad}, |
| booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, |
| year = {2026} |
| } |
| ``` |
