Datasets

Reviewed as of 19 August 2026. Modulate’s published results are measured on the datasets its benchmarks define. Neither corpus set is chosen by Modulate.

Speech DF Arena: 14 deepfake datasets

Speech DF Arena scores deepfake detection across 14 evaluation sets spanning synthesis families, languages, and channel conditions.

Dataset	Attack family	Condition it stresses
ASVspoof 2019	Text-to-speech and voice conversion	Studio-clean synthesis. The long-standing baseline, and the set most detectors are tuned against.
ASVspoof 2021 LA	Text-to-speech and voice conversion	The 2019 attacks after transmission through telephony codecs. Separates codec robustness from detection ability.
ASVspoof 2021 DF	Compressed synthetic speech	Varied lossy encoders and bitrates, as media re-encoding would apply.
ASVspoof 2024 Eval	Crowdsourced and adversarial synthesis	Newer generation methods over non-studio source recordings.
Fake or Real	Commercial text-to-speech	Synthesis from deployed commercial systems, across mixed recording conditions.
Codecfake	Neural audio codec resynthesis	Speech reconstructed through a learned codec rather than a vocoder. Genuine speech enters the pipeline, so there are no synthesis artefacts to key on.
ADD 2022 Track 1	Mandarin full-utterance fakes	Low-quality and noisy Mandarin audio.
ADD 2022 Track 3	Adversarial Mandarin fakes	Attacks constructed to evade detection rather than to sound natural.
ADD 2023 Round 1	Mandarin deepfake detection	Second-edition challenge audio, first evaluation round.
ADD 2023 Round 2	Mandarin deepfake detection	Second evaluation round of the same challenge.
DFADD	Diffusion and flow-matching text-to-speech	Generation families that postdate most detectors’ training data.
LibriVoc	Vocoder artefacts	Multiple vocoders over read speech, isolating the vocoder as the only synthetic signal.
SONAR	Recent end-to-end text-to-speech	Current-generation systems, including synthesis with no separate vocoder stage.
In The Wild	Real-world deepfakes	Deepfaked speech of public figures collected from social media. No controlled synthesis pipeline and no matching training distribution.

In The Wild is the only set drawn from deepfakes made to deceive rather than to populate a corpus. Codecfake is the only set whose audio starts as genuine speech, which defeats detectors keyed to synthesis artefacts. Results: Deepfake Detection.

Open ASR Leaderboard: 8 transcription datasets

The Open ASR Leaderboard scores English transcription across eight corpora, applying one text normaliser to every entrant.

Dataset	Speech type	Condition it stresses
AMI	Multi-party meetings	Far-field microphones, overlapping speech, spontaneous turn-taking. The hardest set on the leaderboard for every entrant.
Earnings-22	Earnings calls	Telephony-grade audio, accented English, dense financial vocabulary and named entities.
GigaSpeech	Mixed podcast, audiobook, and video	Broad-domain spontaneous speech across recording qualities.
LibriSpeech clean	Read audiobooks	Well-recorded read speech. Near-saturated across entrants.
LibriSpeech other	Read audiobooks	The harder speaker split of the same corpus.
SPGISpeech	Financial calls	Long-form professional speech with heavy domain vocabulary.
TED-LIUM	Conference talks	Prepared single-speaker delivery, varied accents.
VoxPopuli	European Parliament proceedings	Non-native English accents and parliamentary register.

Earnings-22 and VoxPopuli are the two sets closest to contact-centre audio. Modulate reports that pair as a named subset alongside the full leaderboard result. Results: Transcription.

What these datasets do not cover

8 kHz telephony throughout. ASVspoof 2021 LA applies telephony codecs and Earnings-22 is telephony-grade, but most sets in both benchmarks are wideband. A pipeline running narrowband PCM end to end is outside the measured distribution.
Non-speech audio between speech. IVR prompts, hold music, ringback, and transfer tones are absent from every set listed here.
Streaming. Both benchmarks score complete files. Neither measures partial-result latency or accuracy on a live socket.
Languages beyond the sets listed. Speech DF Arena covers English and Mandarin. The Open ASR Leaderboard is English only. Neither speaks to the rest of the languages Multilingual Transcription accepts.
Speaker diarization, emotion, accent, and PII/PHI. No public benchmark in this tab scores them. Modulate evaluates them internally.

Benchmarks

Approach

Results

Speech DF Arena: 14 deepfake datasets

Open ASR Leaderboard: 8 transcription datasets

What these datasets do not cover

​Speech DF Arena: 14 deepfake datasets

​Open ASR Leaderboard: 8 transcription datasets

​What these datasets do not cover

Speech DF Arena: 14 deepfake datasets

Open ASR Leaderboard: 8 transcription datasets

What these datasets do not cover