Datasets:

clearspeed
/

WordSpoof

---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
license:
  - cc-by-4.0
  - cc0-1.0
language:
  - en
task_categories:
  - audio-classification
tags:
  - synthetic-speech-detection
  - audio-deepfake-detection
  - anti-spoofing
  - text-to-speech
  - voice-cloning
size_categories:
  - 100K<n<1M
pretty_name: WordSpoof
extra_gated_prompt: >-
  WordSpoof is CC BY 4.0. The compilation and every synthetic clip are CC BY
  4.0, so training, fine-tuning, evaluation and benchmarking are permitted for
  any purpose, commercial included; the only condition is attribution. The
  bona fide clips retain their upstream terms, which are at least as
  permissive: CC BY 4.0 for the Speech Commands subset, CC0 1.0 for the Common
  Voice subset. By requesting access you agree that you will attribute the
  dataset and its source corpora as set out in this card and in NOTICE.md;
  that you will honour the per-provider obligations in NOTICE.md, which travel
  with the data and include attribution and non-endorsement terms; and that
  you will not use any of it to impersonate any person, nor for fraud or
  deception.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
  I agree to the per-provider obligations in the NOTICE file: checkbox
---
# Dataset Card for WordSpoof

A public corpus of **standalone single-word** bona fide and synthetic speech,
for research on synthetic-voice detection. Most audio-deepfake corpora are
sentence-length; this one is not, and detectors trained on sentence-length
audio degrade sharply on it.

## Dataset Details

### Dataset Description

WordSpoof pairs real single-word utterances with synthetic ones generated by
10 modern text-to-speech and voice-cloning systems, so a detector — or a
researcher auditing one — can measure what happens when the input shrinks from
a sentence to a word. Both classes are English, drawn from `single_word` and
`yes_no` response kinds, and shipped as 16 kHz mono PCM16 WAV with a per-clip
JSON manifest.

- **Curated by:** Mario Gómez-Martínez (Clearspeed) and James Gibson (Clearspeed)
- **Funded by:** Clearspeed
- **Shared by:** Clearspeed
- **Language(s) (NLP):** English (en)
- **License:** **CC BY 4.0** over the compilation and every synthetic clip —
  commercial use permitted, attribution required. The bona fide clips retain
  their upstream CC BY 4.0 (Google Speech Commands v2) and CC0 1.0 (Mozilla
  Common Voice) terms, which are at least as permissive. Full breakdown, the
  legal reasoning, and per-provider obligations are in **Licence and Legal
  Notices** below — read that section before redistributing.

### Dataset Sources

- **Repository:** this HuggingFace dataset and a permanent archive copy at Zenodo, DOI
  [10.5281/zenodo.22763850](https://doi.org/10.5281/zenodo.22763850). Both copies are byte-identical.
- **Paper:** ICASSP 2027 submission titled *"WordSpoof: a public dataset for synthetic-voice detection on single-word utterances"*,
  citing this dataset by its sha256. Citation details below.

**Verify what you downloaded.** `SHA256SUMS.txt` ships beside these files and
covers all of them:

```bash
shasum -a 256 -c SHA256SUMS.txt
```

The published artifact is:

```
WordSpoof.tar
sha256  6c31dfc242279456d71b6c50e2e14322f6418437933bc5caf0b779219dc9921c
bytes   9,532,989,440
built   2026-09-13
```

Any paper reporting numbers on this corpus should cite that sha256, not the
name — see **Citation** below for why.

## Uses

### Direct Use

Training, fine-tuning, evaluating, or benchmarking synthetic-voice detectors,
**for any purpose, commercial or not**. The publisher adds no no-training term
and no non-commercial term; the only condition is attribution. See **Licence
and Legal Notices** for the exact per-subset terms, and **NOTICE.md** for the
per-provider obligations that travel with the data.

The dataset is well suited to studying how detectors trained on sentence-length
audio behave on single-word or short-utterance input, and to auditing
content-blind shortcuts in existing detectors (see **Bias, Risks, and
Limitations**, which documents one such shortcut measured on this corpus).

### Out-of-Scope Use

- **Use without attribution.** CC BY 4.0's one condition. The required form is
  in **Licence and Legal Notices**, and several generators add their own
  attribution and non-endorsement terms in **NOTICE.md**.
- **Impersonation.** No clip may be used to impersonate a real person; every
  cloning reference voice is a consenting, published research speaker (VCTK),
  and every synthetic clip is labelled `spoof`.
- **Voice-cloning misuse** of any kind, per the gating terms above.

## Dataset Structure

```
WordSpoof/
  audio/<audio_id>.wav      one clip per file, 16 kHz mono PCM16
  manifest.jsonl            one JSON object per clip
  prompts.json              the prompt set
  release_meta.json         build provenance
  LICENSE
  NOTICE.md                 per-provider obligations and source attributions
  licenses/                 archived licence texts
```

Extract with `tar -xf WordSpoof.tar`, which yields a single `WordSpoof/`
directory.

`release_meta.json` carries both names: `"release_name": "WordSpoof"` and
`"build_id": "svd_short_public_v1"`. WordSpoof is what this dataset is called;
the build id is the internal build the bytes came from, kept so the
`query_sql` and `source_table_versions` in that file stay attributable to a
real build.

Each `manifest.jsonl` row carries `audio_id`, `audio_path`, `path_scope`,
`dataset`, `split`, `label`, `lang`, `response_kind`, `prompt_id`,
`prompt_text`, `prompts_revision_id`, `source_dataset`, `provider`,
`provider_version`, `voice`, `voice_ref_source`, `take_idx`, `recipe_id`,
`recipe_version`, `duration_s`, `sample_rate`, `codec`, and a per-clip
`sha256`.

Three of those carry more weight than their names suggest, and the guidance in
this card depends on them:

- **`recipe_id`** — the channel-conditioning recipe applied to the clip, and
  **empty when none was**. It is how you separate the 33% of spoof clips that
  ship as raw generator output from the 67% that were conditioned. Both
  **Bias, Risks, and Limitations** and the defect manifest turn on this
  distinction.
- **`take_idx`** — which of the repeated generations for a given (voice,
  prompt) this clip is. Needed to treat (voice, prompt) as the sampling unit
  rather than the clip, which **Recommendations** asks you to do for `kokoro`.
- **`path_scope`** — always `tar_relative` here, meaning `audio_path` resolves
  inside this archive.
| | |
|---|---:|
| clips | **207,173** |
| bona fide | 138,555 |
| spoof | 68,618 |
| spoof systems | **10** |
| language | English |
| audio | 80.1 h · 16 kHz mono · PCM16 WAV |
| response kinds | `single_word`, `yes_no` |
| splits | train 168,375 · val 19,594 · test 19,204 |
**Bona fide** clips come from Google Speech Commands v2 (`gsc_v2`) and the
single-word English segment of Mozilla Common Voice 7.0
(`cv_singleword_7_0_en`).
**Spoof** clips are generated by this project from 10 systems, a mix of
preset-voice and zero-shot cloning:

| generator | clips | | generator | clips |
|---|---:|---|---|---:|
| `qwen_base` | 15,488 | | `piper` | 1,887 |
| `cosyvoice2` | 15,130 | | `kokoro` | 1,863 |
| `voxcpm2` | 14,996 | | `qwen_cv` | 1,203 |
| `zonos` | 14,283 | | `openai` | 759 |
| `chatterbox` | 2,334 | | `magpie` | 675 |

Total 68,618. The cells are **very unequal** — the four largest carry 87% of
the spoof side, and `magpie` has 675 clips against `qwen_base`'s 15,488. A
pooled number over this corpus is dominated by four generators, so report
per-generator results alongside any pooled figure.

**Licence compatibility is a selection criterion for this set, not an
afterthought.** Every generator here is licensed such that its outputs can be
redistributed and used commercially, and such that no obligation it imposes
has to be passed on to you beyond the attribution and non-endorsement terms in
`NOTICE.md`. A model whose terms reach its own outputs with a condition
CC BY 4.0 §2(a)(5)(B) forbids us from imposing on you cannot appear in this
corpus, however good it sounds.

For `kokoro`, the clip count is **not** a sample size — see **Bias, Risks, and
Limitations**.

## Dataset Creation

### Curation Rationale

Most public audio-deepfake corpora are sentence-length. Deployed voice
interfaces frequently are not — a spoken PIN digit, a "yes", a wake word. This
corpus exists to measure detector behaviour on that shorter, differently
structured input, and to do so with a public, redistributable artifact so
published results can be reproduced independently.

### Source Data

Cloning references come only from the VCTK corpus (CC BY 4.0, CSTR University
of Edinburgh) — consenting, published research speakers. No private
individual's voice was cloned. Every synthetic clip is labelled `spoof`.

#### Data Collection and Processing

Every spoof clip is scored with CTC ASR for **structural** defects — empty
audio, runaway duration, or more content than was asked for.

Measured over **all 68,618 spoof clips in this release**, on two axes, because
the first one structurally cannot see the second:

| generator | shipped | generator-defect | quiet as shipped | either | either % |
|---|---:|---:|---:|---:|---:|
| `qwen_cv` | 1,203 | 115 | 2 | 115 | 9.56% |
| `cosyvoice2` | 15,130 | 506 | 571 | 908 | 6.00% |
| `openai` | 759 | 27 | 37 | 38 | 5.01% |
| `chatterbox` | 2,334 | 62 | 1 | 63 | 2.70% |
| `voxcpm2` | 14,996 | 9 | 71 | 80 | 0.53% |
| `qwen_base` | 15,488 | 1 | 77 | 78 | 0.50% |
| `piper` | 1,887 | 7 | 0 | 7 | 0.37% |
| `magpie` | 675 | 1 | 0 | 1 | 0.15% |
| `zonos` | 14,283 | 10 | 9 | 19 | 0.13% |
| `kokoro` | 1,863 | 0 | 0 | 0 | 0.00% |
| **total** | **68,618** | **738** | **768** | **1,309** | **1.91%** |

**Axis 1, generator defects — 738 clips (1.08%).** The three rules above,
scored on raw generator output.

**Axis 2, near-silence as shipped — 768 clips (1.12%).** Peak amplitude below
0.05 in the delivered WAV. The audit cannot see these: its "empty" rule
thresholds voiced frames *relative to each clip's own maximum*, so a uniformly
quiet clip is never "empty". Only 197 clips appear on both axes.

**Most near-silence is not the generator's fault**, and the manifest says so
per clip. Of the 768, only 197 had raw audio the audit also condemned; the
other 571 left the generator clean and were attenuated downstream — 501 of
them under the `gscv2_noise_mp3_v1` conditioning recipe. They are listed
anyway, because a near-inaudible clip is useless to you whichever stage made
it quiet.

**`cosyvoice2` and `qwen_cv` are where the defects concentrate.** Between them
they carry 1,023 of the 1,309, on 24% of the spoof side. If you filter one
cell, filter `cosyvoice2`. Three generators — `piper`, `kokoro`, `magpie` —
are at or below 0.37%, and `kokoro` has none at all.
**You can subtract them.** A per-clip list ships beside the tarball:
    WordSpoof_defects.csv     1,309 rows
    WordSpoof_defects.README.md
Join it on `audio_id` against `manifest.jsonl` and drop the matches. What
remains is **67,309 spoof clips and 205,864 rows in total, with all ten
generators retained** — bona fide clips are untouched. The `basis` column
lets you subtract one axis rather than both.

**The false-positive floor of the test itself.** A defect
rate means little without knowing how often the test is wrong. The same
structural rule layer was run over **4,482 real human clips** from the Speech
Commands **test** split — the entire eligible pool, excluding the corpus's own
`_silence_` and `_background_noise_` labels:

    rejected: 1 of 4,482  =  0.022%
This is a property of the rule layer and of the bona fide corpus rather than
of the spoof clips. One caveat stated precisely: the release ships Speech Commands'
**train/val** split (105,829 clips) and this was measured on its **test**
split, so it is a held-out estimate from the same corpus and recording
conditions rather than a measurement of the shipped clips themselves.

#### Who are the source data producers?

Bona fide speech: Google (Speech Commands v2) and the Mozilla Common Voice
project's contributors (single-word English segment, Common Voice 7.0).
Cloning reference voices: consenting, published speakers from the VCTK corpus
(CSTR, University of Edinburgh). Synthetic speech: 10 TTS/voice-cloning
systems listed in **Dataset Structure**, run by the dataset curators.

### Annotations

Not applicable in the crowd-annotation sense. Labels (`bonafide` / `spoof`)
are assigned at generation time by construction, not by human annotators.
Structural-defect verdicts (see **Data Collection and Processing**) are
produced by an automated CTC-ASR-based audit, not by manual annotation.

#### Personal and Sensitive Information

**The bona fide clips are not filtered and carry no defect list.** A glitchy
synthetic clip is a generation failure; a noisy human recording is a real
recording, which is what a bona fide corpus is for.

No private individual's voice was cloned. Cloning references come only from
consenting, published research speakers (VCTK). Every synthetic clip is
labelled `spoof`, satisfying disclosure obligations that attach to cloning use
(see **Licence and Legal Notices**). We are not aware of any personal,
sensitive, or private information in the bona fide source corpora beyond what
their own publishers (Google, Mozilla) have already disclosed and licensed for
redistribution.

## Bias, Risks, and Limitations

**The corpus carries a measured low-level shortcut.** A classifier can
separate spoof from bona fide using content-blind features alone — no ASR, no
transcript, no vocabulary. Measured on the clips in this tarball, 400 per
(provider × conditioning) cell sampled deterministically by `audio_id` hash,
5-fold cross-validated AUC:

| subset | all features | framing only | acoustics only |
|---|---:|---:|---:|
| `single_word` | 0.9419 | 0.8510 | 0.8821 |
| **`yes_no`** | **0.9812** | **0.8631** | **0.9401** |
"Framing" is duration, leading and trailing silence, and speech span;
"acoustics" is everything else — energy, spectral tilt, edge behaviour. Both
groups separate the classes well on their own, so this is not a single fixable
artefact.
**`yes_no` is the worse cell, and the reason is corpus composition, not the
generators.** Its bona fide side is a **single corpus of 4,868 clips** from one
recording channel (`cv_singleword_7_0_en`), where `single_word` draws on two
corpora and 133,687 clips (`gsc_v2` 105,829 + `cv_singleword_7_0_en` 27,858).
A narrower real-speech distribution is easier to separate from anything. Per
spoof cell, against pooled bona fide, all 20 cells of each response kind land
between **0.9155 and 1.0000** — so no single generator is carrying this.

**Clip duration is a provider fingerprint on the unconditioned clips, and for
one generator it is exact.** Fraction of clips whose duration is an exact
multiple of 0.16 s — the frame period of a 6.25 Hz acoustic token rate —
measured on the shipped WAVs:

| generator | unconditioned | conditioned |
|---|---:|---:|
| `voxcpm2` | **100.0%** (5,004) | 1.7% (9,992) |
| `qwen_base` | 49.0% (5,162) | 1.7% (10,326) |
| `qwen_cv` | 47.1% (397) | 3.3% (806) |
| `chatterbox` | 23.9% (778) | 2.5% (1,556) |
| every other generator | ≤ 6.3% | ≤ 2.7% |

A duration is 16 kHz sample-quantised, so 0.16 s is 2,560 samples and the
chance rate is 1/2,560 = **0.04%**. Against that, `voxcpm2`'s unconditioned
clips are a **perfect** rule: `duration mod 0.16 s == 0` identifies them
without listening. `qwen_base` and `qwen_cv` sit near 50% because their token
rate is twice as fast, so half their clips coincide with the same grid.

Conditioning destroys the pattern — every conditioned cell falls to the
1-3% range — because the noise recipes repad the clip. **So this affects the
22,863 spoof clips (33%) that ship unconditioned.** Anything you train should be
checked against a duration-only baseline, and if you need the effect gone,
drop the unconditioned spoof rows (`recipe_id` is empty for them in
`manifest.jsonl`).

**Near-silence is unevenly distributed across generators, which is a label
rule of the same kind.** 768 spoof clips ship with peak amplitude below 0.05,
and the rate per generator runs from 4.87% (`openai`) and 3.77%
(`cosyvoice2`) down to 0.00% (`piper`, `kokoro`, `magpie`). Most of it is
introduced by conditioning rather than by the generator — 571 of the 768 had
clean raw audio — but a detector cannot tell the difference and neither can a
downstream metric. Every one of those clips is enumerated in
`WordSpoof_defects.csv` so you can subtract them; see **Data Collection and
Processing**.

**`kokoro` rows are not three independent samples.** `kokoro` was generated
with three takes per (voice, prompt), as the other generators were. For
`kokoro` those three are **not three independent samples**, and the reason is
architectural rather than a property of any particular batch. Read from the
`kokoro` 0.9.4 package: `KModel.forward(phonemes, ref_s, speed)` is its entire
inference surface — no seed, no temperature, no sampling argument anywhere in
it. Duration is a deterministic integer frame count,
`round(sigmoid(duration).sum(-1) / speed)`, and the style vector is a fixed
voicepack lookup. **There is no way to ask `kokoro` for a different reading of
a prompt.** So sibling takes share the same words, voice, prosody, F0 contour,
phoneme alignment and generated duration by construction; what differs is the
decoder's random harmonic phase, which `eval()` does not disable. A
re-excitation of one performance, not a second performance. The other
generators here expose a seed or a temperature, so this is specific to
`kokoro` rather than a property of the take protocol.

Two consequences for anything you compute on the `kokoro` cell:

- **Its effective sample size is about a third of its clip count.** The 1,863
  `kokoro` clips carry about **621 independent utterances** — exactly 3.0
  clips per unit. Treat (voice, prompt) as the unit, not the clip; a standard
  error computed per clip is understated by roughly √3.
- **Near-duplicates span the train/test split.** The split is assigned per
  clip and the three takes are distinct clips, so they can land on different
  sides. A detector trained on the train split has seen a re-excitation of
  some test utterances. This is not an identity leak and the audio is not
  identical — but a `kokoro` test number is optimistic by an unmeasured
  amount.

Whether this is a defect depends on what you are measuring. A spoof detector
keys largely on vocoder artefacts, and a re-randomised excitation is arguably
a fresh draw from that distribution. For prosody, duration or content-level
analysis it is not. **Only `kokoro` is affected** — `magpie`'s and `openai`'s
handful of duration-matching cross-split pairs were checked at byte level and
are coincidence, 93-99% of their samples differ. This is documented rather
than filtered so you can decide for yourself; a future build caps `kokoro` at
one take per combo.

**OpenAI is the only commercial API among the ten generators.** Detector
results measured here may therefore not transfer to the strongest commercial
cloning services, and work using this corpus should state that limit.

The structural defect test does **not** catch identity miss — a fluent,
correctly-timed clip that says the *wrong words*. That is a separate axis and
it is not measured in this release.

### Recommendations

- **A duration-only or acoustics-only baseline is mandatory** for any result
  claimed on this corpus. The shortcut above is measured, not estimated, and
  it is reported rather than filtered out, so its size is known to you before
  you build on the data. Acoustics alone reach 0.88 and 0.94 AUC; a model that
  does not beat that has learned nothing about synthesis.
- **Check any result against the duration grid**, or drop the unconditioned
  spoof rows. `voxcpm2`'s unconditioned clips are identifiable from their
  duration alone, exactly.
- **Report `single_word` and `yes_no` separately.** They are 0.04 apart on the
  shortcut measurement, which is larger than most reported improvements, and
  pooling them hides it.
- **Use the defect manifest** (`WordSpoof_defects.csv`) if you need the clean
  spoof subset — and decide which axis you care about. Generator defects and
  near-silence are separate columns because 571 of the 768 near-silent clips
  were attenuated by conditioning rather than mis-generated.
- **Report per-generator figures alongside any pooled one** — the generator
  cells are very unequal (87% of the spoof side from four generators), and
  defect rates range from 0.00% to 9.56% across them.
- **Treat `kokoro`'s (voice, prompt) pair as the sampling unit**, not the
  clip, when computing standard errors or designing a train/test split.

## Licence and Legal Notices

**This release is CC BY 4.0**, with one carve-out that is *more* permissive,
not less. The `provider` and `source_dataset` fields in `manifest.jsonl` tell
you which clips are which.

| what | licence | clips |
|---|---|---:|
| the compilation — manifest, prompt set, split assignment, selection and arrangement | **CC BY 4.0** | — |
| every **synthetic** clip | **CC BY 4.0** | 68,618 |
| **bona fide** from Google Speech Commands v2 (`gsc_v2`) | **CC BY 4.0** | 105,829 |
| **bona fide** from Common Voice single-word EN (`cv_singleword_7_0_en`) | **CC0 1.0** (public domain) | 32,726 |

Licence texts are in `licenses/`.

> **Identify your copy by sha256.** This release is
> `6c31dfc242279456d71b6c50e2e14322f6418437933bc5caf0b779219dc9921c`,
> and the licence text that governs it ships inside the tarball as `LICENSE`.
>
> **You do not have to take our word for the bona fide half.** The check is
> outside this release: Google publishes Speech Commands v2 under CC BY 4.0
> and Mozilla publishes the Common Voice single-word English segment under
> CC0 1.0.

**Why a single permissive licence is available here.** **CC BY 4.0
§2(a)(5)(B)** bars a licensee from imposing additional or different terms on
licensed material. So a generator whose own licence reaches its outputs with a
condition that would bind you — a naming requirement on models you train, or a
non-commercial limit — cannot be in this corpus, because we could neither pass
that condition on to you nor honestly stay silent about it. Every generator in
**Dataset Structure** clears that bar on its own terms.

**Why Common Voice stays CC0.** It is public domain, and nothing here claims
rights over it. CC BY 4.0's attribution condition does not attach to those
clips, though the courtesy credit below is given regardless.

**Attribution**, required for the CC BY 4.0 material:

- Warden, P. *Speech Commands: A Dataset for Limited-Vocabulary Speech
  Recognition*, Google, 2018 — for the `gsc_v2` clips.
- Mozilla Common Voice 7.0, single-word English segment — for the
  `cv_singleword_7_0_en` clips.
- This release, cited as below, for the compilation and the synthetic clips.

**Training and commercial use are both expressly permitted.** You may use
these clips to train, fine-tune, evaluate and benchmark models for any
purpose, commercial or not, and you may distribute what you build. The
publisher adds no no-training term and no non-commercial term. **The only
condition is attribution**, in the form given above, plus the per-provider
obligations below — which are attribution and non-endorsement terms, not use
restrictions.

There is no research-versus-product distinction to police here: a checkpoint
trained on this corpus may become a product checkpoint.

**Obligations that travel with the data**, listed per provider in
`NOTICE.md`. In summary:

- All spoof clips are AI-generated and the generating system is named per
  clip. Several generators require this disclosure; the `spoof` label and the
  `provider` field satisfy it.
- Magpie is licensed under the NVIDIA Open Model License, which requires
  attribution on redistribution. NVIDIA claims no ownership of outputs.
- LibriTTS (OpenSLR 60, CC BY 4.0) is attributed for the Piper
  `en_US-libritts-high` voice — Zen, H. et al., *LibriTTS: A Corpus Derived
  from LibriSpeech for Text-to-Speech*.
- Kokoro's model card attributes its training audio to the Koniwa corpus
  (CC BY 3.0) and the SIWIS corpus (CC BY 4.0).
- Chatterbox clips carry Resemble AI's Perth perceptual watermark, on by
  default. This is a **methodology** warning rather than a licence one: a
  detector trained on that partition may learn the watermark instead of
  genuine synthesis artefacts.
**No trained detector checkpoints are released.** That is a scope decision
about this release, not a licence restriction: under CC BY 4.0 you may train
on this corpus and distribute the result, subject to attribution.

## Citation

**BibTeX:**

```bibtex
@misc{gomez2026svdshort,
  author       = {Gómez-Martínez, Mario and Gibson, James},
  title        = {{WordSpoof}: a public corpus of single-word bona fide and
                  synthetic speech},
  year         = {2026},
  publisher    = {Zenodo},
  version      = {v1 (artifact 6c31dfc24227)},
  doi          = {10.5281/zenodo.22763850},
  howpublished = {Zenodo, \url{https://doi.org/10.5281/zenodo.22763850}},
  note         = {sha256 6c31dfc242279456d71b6c50e2e14322f6418437933bc5caf0b779219dc9921c}
}
```

`@misc`, deliberately, not `@dataset`. Several IEEE and ACM styles —
including `IEEEbib.bst`, which ICASSP uses — have no `@dataset` entry type
and **silently drop** `publisher`, `version` and `doi` from the rendered
reference. The `howpublished` field is there so the DOI still renders under
those styles.

**APA:**

Gómez-Martínez, M., & Gibson, J. (2026). *WordSpoof: a public corpus of single-word bona fide and
synthetic speech* (Version v1, artifact 6c31dfc24227) [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.22763850

## Glossary

- **Structural defect** — a spoof clip whose audio is empty, runs away in
  duration, or contains more content than the prompt asked for. Measured with
  CTC ASR on raw generator output. Distinct from **near-silence**, which is
  measured on the shipped bytes and which this test cannot detect; both ship
  in `WordSpoof_defects.csv`. See **Data Collection and Processing**.
- **Identity miss** — a fluent, correctly-timed synthetic clip that says the
  *wrong* words. Not caught by the structural-defect test; not measured in
  this release.
- **Low-level shortcut** — separability between spoof and bona fide using
  content-blind features alone (no ASR, no transcript). Measured on this
  corpus and disclosed in **Bias, Risks, and Limitations**.
- **`response_kind`** — the manifest field distinguishing `single_word`
  clips from `yes_no` clips; report results per kind, not pooled.
- **`build_id`** — the internal build identifier recorded in
  `release_meta.json`, distinct from `release_name` (`WordSpoof`); see
  **Dataset Structure**.

## More Information

The paper this dataset supports is a submission to ICASSP 2027, titled
*"WordSpoof: a public dataset for synthetic-voice detection on single-word utterances"*;
citation details will be added to this card once accepted. For questions
about a specific provider's licence terms, consult `NOTICE.md` inside the
archive first — it is the per-provider source of truth and is
machine-checkable against `manifest.jsonl`'s `provider` field.

## Dataset Card Authors

Mario Gómez-Martínez (Clearspeed) and James Gibson (Clearspeed)

## Dataset Card Contact

mario.gomez@clearspeed.com