Datasets:

IHP-Lab
/

VoxParadox

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

Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox

Jiacheng Pang^*, Ashutosh Chaubey^*, Mohammad Soleymani
University of Southern California
_{^* Equal contribution}

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

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):

python eval.py --predictions preds.jsonl

Example prediction record:

{"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 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

@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}
}