Datasets:

soarhigh
/

mondegreenbench

license: cc-by-4.0
language:
  - en
pretty_name: MondegreensEval
tags:
  - speech
  - automatic-speech-recognition
  - audio
  - bias
  - hallucination
  - whisper
task_categories:
  - automatic-speech-recognition
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: pair_id
      dtype: string
    - name: category
      dtype: string
    - name: source
      dtype: string
    - name: original_text
      dtype: string
    - name: mondegreen_text
      dtype: string
    - name: phoneme_original
      list: string
    - name: phoneme_mondegreen
      list: string
    - name: phoneme_edit_distance
      dtype: int64
    - name: phoneme_edit_distance_norm
      dtype: float64
    - name: condition
      dtype: string
    - name: audio_original
      dtype:
        audio:
          sampling_rate: 16000
    - name: audio_mondegreen
      dtype:
        audio:
          sampling_rate: 16000
  splits:
    - name: test
      num_bytes: 202606959
      num_examples: 1140
  download_size: 198883999
  dataset_size: 202606959
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

MondegreensEval

Audio companion dataset for MondegreensEval: A Phonetic Benchmark for Measuring Language-Model Bias in Automatic Speech Recognition (ICML ML for Audio Workshop, 2026).

Code, evaluation pipeline, and per-model transcription/metric outputs: https://github.com/soarhigh/mondegreenbench

Mondegreens — phonetically near-identical phrase pairs with distinct meanings — expose a measurable failure mode in decoder-based ASR: the model's internal language-model prior can override acoustic evidence and "correct" a spoken mondegreen back to the canonical phrase (e.g. "excuse me while I kiss this guy" → "excuse me while I kiss the sky"). This dataset provides the synthesized audio and phonetic annotations backing that benchmark.

Dataset composition

190 curated mondegreen pairs (114 song lyrics, 41 speech phrases, 35 liturgical/prayer lines), each synthesized as both its canonical (original) and misheard (mondegreen) text, at 6 acoustic conditions (clean + additive Gaussian white noise at SNR = 15, 10, 5, 0, −5 dB) — 190 × 6 = 1,140 rows in the test split.

Why both original and mondegreen audio are included: the benchmark's headline metric (MCR-mono) plays the mondegreen audio and checks whether the model wrongly outputs the canonical phrase — but validating that this reflects a genuine language-model bias (not a generic transcription artifact) requires the symmetric control: playing the original audio and confirming the reverse confusion (MCR-orig) stays low. Both audio columns are needed for either metric.

Column	Type	Description
`pair_id`	string	Unique identifier, e.g. `sg_001`
`category`	string	One of `song_lyrics`, `speech_phrases`, `prayers`
`source`	string	Provenance (song/artist, phrase type, or liturgical source)
`original_text`	string	Canonical, higher-frequency phrase
`mondegreen_text`	string	Phonetically similar, lower-frequency misheard alternative
`phoneme_original`	list[string]	ARPAbet phonemes for `original_text` (stress-stripped)
`phoneme_mondegreen`	list[string]	ARPAbet phonemes for `mondegreen_text` (stress-stripped)
`phoneme_edit_distance`	int	Raw Levenshtein distance between the two phoneme sequences
`phoneme_edit_distance_norm`	float	Distance normalized by the longer sequence's length, in [0, 1]
`condition`	string	`clean`, `snr15`, `snr10`, `snr5`, `snr0`, or `snr-5`
`audio_original`	Audio (16 kHz mono)	TTS-synthesized audio of `original_text` under `condition`
`audio_mondegreen`	Audio (16 kHz mono)	TTS-synthesized audio of `mondegreen_text` under `condition`

Phoneme distances span 0.038–1.000 (mean 0.332), grouped into four tiers used by the paper's analysis: near-homophones (< 0.10, n=36 pairs), plausibly ambiguous (0.10–0.25, n=64), weakly similar (0.25–0.40, n=16), and dissimilar (> 0.40, n=74).

Synthesis details

Audio synthesized via Edge-TTS (en-US-JennyNeural voice), 16 kHz mono WAV. Noisy variants generated by additive Gaussian white noise at the stated SNR, applied deterministically (seed=42) to the clean signal. Phonemes derived from the CMU Pronouncing Dictionary with a g2p_en fallback for out-of-vocabulary words.

Loading

from datasets import load_dataset

ds = load_dataset("soarhigh/mondegreenbench", split="test")

# Only clean audio:
clean = ds.filter(lambda r: r["condition"] == "clean")

# Only the primary diagnostic tier (phonetically ambiguous, 0.10-0.25):
ambiguous = ds.filter(lambda r: 0.10 <= r["phoneme_edit_distance_norm"] < 0.25)

Limitations

See the paper's Discussion/Limitations sections: all audio comes from a single TTS engine and voice (no speaker variability or recorded human speech); noise is additive Gaussian white noise, not an ecologically representative degradation (babble, reverberation, codec artifacts); and each phrase has exactly one synthesized sample, not resampled across multiple stochastic TTS seeds.

Citation

@inproceedings{mondegreenseval2026,
  title     = {MondegreensEval: A Phonetic Benchmark for Measuring Language-Model Bias in Automatic Speech Recognition},
  author    = {Wan Ju Kang}
  booktitle = {ICML 2026 Workshop on Machine Learning for Audio},
  year      = {2026}
}

License

CC BY 4.0. Note that the song_lyrics category quotes short excerpts of song lyrics for research/benchmarking purposes; if you plan to redistribute or build commercial products on this subset specifically, please review the underlying copyright status of the quoted lyrics independently.