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Open Active — Curated and annotated speaker-verification corpora built to order: voiceprint samples tagged to speaker identities and passphrases (clip-to-Speaker-ID and verified-user assignment), vocal-trait annotations (low tone, high pitch, accented speech), quality and mismatch flags on rejected clips, and deliberately-collected hard cases — voice mimics, noisy recordings, distorted audio — escalated to biometric specialists. Stated scale guidance: task scopes around 15,000 speaker samples, 5,000-20,000+ clips per speaker category, a further 3,000-10,000 clips to lift accuracy, hundreds of…
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Curated and annotated speaker-verification corpora built to order: voiceprint samples tagged to speaker identities and passphrases (clip-to-Speaker-ID and verified-user assignment), vocal-trait annotations (low tone, high pitch, accented speech), quality and mismatch flags on rejected clips, and deliberately-collected hard cases — voice mimics, noisy recordings, distorted audio — escalated to biometric specialists. Stated scale guidance: task scopes around 15,000 speaker samples, 5,000-20,000+ clips per speaker category, a further 3,000-10,000 clips to lift accuracy, hundreds of thousands of clips to start a verification model and millions at large-scale deployment
Scale The page gives the vendor's own scale guidanceCoverage Information TechnologyAsset class Equities
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Open Active — Curated and annotated speaker-verification corpora built to order: voiceprint samples tagged to speaker identities and passphrases (clip-to-Speaker-ID and verified-user assignment), vocal-trait annotations (low tone, high pitch, accented speech), quality and mismatch flags on rejected clips, and deliberately-collected hard cases — voice mimics, noisy recordings, distorted audio — escalated to biometric specialists. Stated scale guidance: task scopes around 15,000 speaker samples, 5,000-20,000+ clips per speaker category, a further 3,000-10,000 clips to lift accuracy, hundreds of…
Open Active offers (Alternative, Reference) — The page gives the vendor's own scale guidance, which is the volume answer: hundreds of thousands of clips to start a verification model, 5,000-20,000+ clips per speaker category, 3,000-10,000 more to improve accuracy, and millions of clips in large-scale deployments. Across engagements this implies tens of millions of annotated speaker clips have passed through the workflow, though ownership of each corpus is per-client.
Training and benchmarking speaker-verification and voice-print biometric systems, liveness and anti-spoofing model development using the labelled mimic set, false-rejection-rate reduction across accents and vocal traits, voice-authentication fraud detection for banking and secure communications, on-device voice unlock for smart devices, and adversarial evaluation suites for voice-biometric security
Coverage spans US; Application Software; alternative, reference; equities.
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