soarhigh/mondegreenbench

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.