besimple-ai/vocal-affect-bench

VocalAffectBench

VocalAffectBench is a test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio.

Paper: VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models

The benchmark targets the expressed emotion — what the speaker conveys through vocal tone, prosody, pace, intensity, and pauses — not inferred internal state.

Contents

• 280 human-recorded English WAV clips, totalling 2.32 hours.
• 7 emotion classes, balanced at 40 clips each: angry, disgusted, fearful, happy, neutral, sad, surprised.
• 6 tracked baseline model outputs.
• Audio recorded at 16 kHz mono.

Released files:

• audio/*.wav — benchmark audio files, named by audio_id.
• data/metadata.jsonl — per-clip metadata: emotion label, duration, sample rate.
• data/audio-metadata.csv — flat CSV version of clip metadata.
• data/predictions.csv — all baseline model predictions.
• data/leaderboard-summary.csv — aggregate baseline leaderboard.
• baselines/predictions/*.csv — per-model prediction files.
• baselines/results.csv — aggregate leaderboard (same as above).
• paper/ — paper PDF and LaTeX source.

The public release does not include transcripts, script text, speaker identities, or demographic metadata.

Task

Each item is an audio clip of a person expressing one of the seven target emotions. Models are evaluated under a raw-audio-only protocol — the model receives only the audio file and a fixed prompt listing the allowed labels. No transcripts, speaker metadata, domain hints, or post-processing are permitted in the main setting.

The prompt instructs the model to:

Choose exactly one primary expressed emotion from the allowed label set. Base your answer only on the expressed vocal tone, prosody, pace, intensity, pauses, and wording. Do not infer the speaker's private internal state.

Label Set

angry  |  disgusted  |  fearful  |  happy  |  neutral  |  sad  |  surprised

Metrics

VocalAffectBench reports overall accuracy (correct / total scored clips). Because the benchmark is balanced at 40 clips per class, overall accuracy equals macro accuracy — no separate macro metric is needed.

Baselines

Current tracked baselines (6 models):

Rank	Model	Overall Accuracy
1	gemini_3_5_flash	44.3%
2	hume_prosody	38.0%
3	tr_qwen3_5_omni_plus	37.9%
4	tr_voxtral_small	34.3%
5	inworld_voice_profile	28.6%
6	openai_realtime	27.9%

All baselines use the raw-audio-only protocol with no transcripts or post-processing.

Data Format

metadata.jsonl

Each line is a JSON object:

{
  "audio_id": "06Eg9dXO99fAAti4HB34",
  "file_name": "audio/06Eg9dXO99fAAti4HB34.wav",
  "required_emotion": "neutral",
  "duration_seconds": 12.4,
  "sample_rate": 16000,
  "channels": 1
}

predictions.csv

audio_id, required_emotion, model_name, provider_model, predicted_label, mapped_label, confidence, correct, error

• predicted_label — raw label returned by the model.
• mapped_label — normalised to the 7-class label set.
• correct — true / false.

Use and Limits

VocalAffectBench is intended for diagnostic evaluation, provider comparison, and regression tracking of audio emotion models. It is not intended as a training corpus, hidden leaderboard, universal emotion-quality measure, or biometric dataset.

Emotion labels reflect the expressed vocal performance, not speaker demographics or inferred mental states.

Citation

@misc{vocalaffectbench2026,
  title  = {VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models},
  author = {Debaupte, Luc and Baumgartner, Tyler and Tai, Brandon and Fan, Candice and Wang, Bill and Zhong, Yi},
  year   = {2026},
  note   = {Benchmark dataset}
}

License

VocalAffectBench is released under the MIT License.