BLEEP is released for non-commercial academic research only, under a Data Use Agreement. You agree not to (a) attempt to re-identify any speaker; (b) use the recordings for voice cloning, speaker verification, surveillance, or harassment; (c) redistribute the audio or metadata; (d) use the data commercially. Commercial licences are separate and administered by the maintainer. The dataset contains strong profanity and sexually explicit lexical items.

BLEEP — Broadcast Language Elicitation and Evaluation for Profanity

v2.0 · 8,312 clips · 86 speakers · English (US/UK) · 16 kHz mono · 4.62 h

Isolated-word English corpus for profanity speech research. 20 profanity keywords and 29 hard negatives — minimal-pair confusables selected by CMUdict phoneme edit distance and SUBTLEX-US/UK frequency. Each speaker recorded all 49 words twice in one session, once in a neutral and once in an expressive register.

⚠️ Content warning — every clip is a spoken profanity or a near-homophone of one. 🔒 Gated, research-only, non-commercial under a Data Use Agreement.

Composition


Clips / speakers	8,312 / 86 (`ID00001`–`ID00086`), mean 96.7 each
Vocabulary	49 words — 20 profanity, 29 hard negatives
Registers	`neutral` 4,169 · `expressive` 4,143
Labels	3,384 `profanity` / 4,928 `non_profanity`
Audio	16 kHz mono 16-bit WAV, fixed 2.000 s, word onset at 200 ms
Countries	US 49 / UK 37 · collected 20–30 June 2026 via Prolific

Word list

12 base words, 8 morphological variants, 29 hard negatives — 49 in total. Hard negatives are at phoneme edit distance d = 1 from their base word in every case except mustard (d = 2).

Base word	Variants	Hard negatives
fuck	fucking, fucked, fucker	duck, luck, suck, buck
shit	bullshit, shitty	sit, shot, shut, ship, sheet
cunt	—	count, hunt
piss	—	miss, kiss, piece, pick
tits	—	bits, hits, sits, tips
motherfucker	motherfucking	none
cocksucker	—	none
bitch	bitches, bitching	beach, pitch, witch, ditch
ass	—	gas, pass, mass, lass
pussy	—	pushy
bastard	—	mustard
asshole	—	none

Base words and variants are label = profanity; hard negatives are label = non_profanity. All 49 words are recorded in both registers. cocksucker, motherfucker and asshole have no hard negatives — no CMUdict+SUBTLEX neighbours exist at d ≤ 2.

Not included: silence/background and unknown/filler classes, connected speech, codec- or noise-processed variants, TTS augmentation, phone alignments.

Files

clips/ID000NN/ID000NN_{word}_{register}.wav
metadata.csv       # HF loader index (file_name + manifest columns)
manifest.csv       # clip_path, speaker_id, word, register, tier, label,
                   # word_onset_s, word_offset_s, clip_ms
demographics.csv   # speaker_id, gender, age_range, background, accent_dialect,
                   # country, languages, english_acquisition_age, media_exposure

label ∈ {profanity, non_profanity}. word_onset_s and word_offset_s are in the source recording's timebase. Clips are cut at onset − 200 ms with fixed 2 s length; within a clip the word spans [200 ms, 200 + (offset − onset)]. Windows extending past the source are zero-padded.

Demographics are self-reported and categorical. Free-text region was dropped and generalised to country. accent_dialect is pipe-delimited multi-select; an empty value is a non-response.

Speakers

Field	Distribution (n = 86)
`gender`	Female 45 · Male 39 · Non-binary 2
`age_range`	36–45: 26 · 26–35: 23 · 46–55: 16 · 18–25: 12 · 56–65: 8 · 65+: 1
`country`	US 49 · UK 37
`background`	white 48 · black 20 · hispanic/latino 5 · south asian 5 · MENA 2 · mixed 2 · asian 1 · unstated 3
`languages`	english_only 74 · english_plus 8 · unstated 4
`english_acquisition_age`	from birth 72 · before 5: 6 · age 5–10: 2 · age 18+: 1 · unstated 5
`media_exposure`	mostly US 43 · mostly UK 19 · mixed 17 · unstated 7

accent_dialect is multi-select: 116 tags across 86 speakers in 29 distinct combinations. The column below sums to 116, not 86.

Accent / dialect	Speakers
General American	26
Southern US	16
Standard Southern British / RP-like	12
African American English	10
New York / Northeast US	9
Northern England	9
Western US / California	6
Midlands English	6
London Estuary	5
Midwest US	4
Scottish English	4
Hispanic/Latino English	3
Welsh English	2
Northern Irish English	2
Asian American English	1
Unknown	1

Collection and QC

Recorded through a web app (Chrome desktop) in ~8–10 min: mic check, 49 words neutral, 49 words expressive, demographic questionnaire. Prompt order was randomised per speaker. Each trial captured 2.5 s at 48 kHz — a 500 ms lead-in followed by a 2 s speak window. Participants could skip any word or stop early without affecting payment. The expressive prompt read "as in a TV or film scene, never shouted or directed at anyone".

QC was run independently of alignment: Silero VAD localisation (threshold 0.5), duration, clipping and segmental-SNR checks (flag only), and Whisper large-v3 with a primed initial prompt for substitution detection. A recording was rejected only where VAD found no word and Whisper did not confirm it. Speakers retaining fewer than 60 clips were excluded.

Of 9,286 recordings: 8,312 accepted (89.5%), 865 dropped with an excluded speaker (9 speakers), 109 rejected individually.

Alignment used MFA 3.x with the english_us_arpa acoustic model and a supplementary profanity dictionary.

Uses

Permitted: profanity keyword-spotting research; phonetic confusability and false-alarm benchmarking; research on ASR profanity suppression.

Prohibited under the DUA: voice cloning, TTS, and speaker verification on any released speaker; speaker re-identification; surveillance and harassment; commercial use; redistribution; training offensive-content generators.

Consent — what participants were shown

Participants read an information sheet and ticked each item separately before recording. Responses were logged with the document version and a timestamp.

Required consent items:

#	Item
1	Aged 18+, has read and understood the document
2	Understands the recordings involve profane language and is willing to produce it; may skip, stop, or withdraw
3	Explicit consent to biometric processing under UK GDPR Art. 9(2)(a), including transfer to the controller in India and to DUA recipients
4	Grants a perpetual, worldwide, non-exclusive licence
5	Understands the dataset may be commercially licensed (excluding synthetic derivatives) with no further compensation
6	Understands and accepts the re-identification risk
7	Consents to demographic information being collected, stored alongside the recordings, and disclosed to approved researchers and reviewers

Licence

Research-only, non-commercial, gated release, per person, under a Data Use Agreement — not CC-BY, and redistribution is prohibited. The maintainer holds a non-exclusive licence from each speaker and separately administers commercial licences covering the original recordings only, excluding synthetic derivatives. Re-releasing openly under CC-BY would require re-consent, and an erasure request can remove a speaker from a released version.

Maintainer: Ritin Raveendran Kasthuri — contact@methodosprojects.org

Usage

from datasets import load_dataset, Audio
import numpy as np

# Gated: requires an approved access request and `hf auth login`.
# The corpus is one undivided partition, named "train" by convention. No splits are shipped.
ds = load_dataset("<org>/BLEEP", split="train").cast_column("audio", Audio(sampling_rate=16_000))

# Speaker-disjoint split.
spk = sorted(set(ds["speaker_id"]))
np.random.default_rng(0).shuffle(spk)
n = len(spk)
test, dev = set(spk[: n // 7]), set(spk[n // 7 : 2 * n // 7])
train = set(spk) - test - dev
splits = {k: ds.filter(lambda r, s=s: r["speaker_id"] in s)
          for k, s in [("train", train), ("dev", dev), ("test", test)]}

# Word extent within a clip.
word_span_ms = lambda r: (200.0, 200.0 + (r["word_offset_s"] - r["word_onset_s"]) * 1000)

Citation

@misc{kasthuri2026bleep,
  title   = {{BLEEP}: Broadcast Language Elicitation and Evaluation for Profanity},
  author  = {Kasthuri, Ritin Raveendran},
  year    = {2026},
  version = {2.0},
  note    = {English profanity keyword-spotting corpus with systematic phonetic hard
             negatives. Gated research-only release under a Data Use Agreement.},
  howpublished = {\url{https://huggingface.co/datasets/<org>/BLEEP}}
}

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