Clinical-grade speaker separation
Reliably distinguish clinicians, patients, family members, and clinical staff across multi-speaker exam rooms. Built to handle the real audio conditions of consultations.
Persistent voiceprints for clinical staff
Associate a voiceprint with each provider to track them across recordings. The foundation of reliable cross-encounter attribution.
Confidence scoring that reduces documentation errors
Surface per-segment reliability, so your scribe knows which turns to trust and which to flag for review. Lower confusion rates, fewer doc errors, and less time spent correcting notes.
Deployment flexibility for healthcare infrastructure
Built to fit HIPAA-aligned architectures, data residency requirements, and the latency demands of real-time clinical workflows.
Use cases
AI medical scribes: Reliable separation of clinician, patient, family, and staff for correctly attributed clinical notes
Ambient clinical intelligence: Real-time speaker metadata for in-room conversations, including multi-provider and team-based care
Telehealth & virtual consultations: Speaker attribution for video consultations, with patient/provider separation even in poor-bandwidth audio
Dental & multi-staff practices: Track multiple staff roles (assistant, hygienist, provider, coordinator) and label patients as anonymous speakers
Specialty consultations: Speaker continuity across complex, multi-party clinical conversations, rounds, case reviews, and tumor boards
Prescription & medication workflows: Speaker-attributed records for voice-driven prescription, refill, and medication reconciliation flows
Features
Real-time speaker diarization
Sub-second speaker attribution, across multi-speaker exam rooms.
Voiceprints
Persistent voice identity, so the clinician is recognized across every recording.
Voice activity detection
Strip silence, ambient hallway noise, and non-speech regions.
Overlapping speech detection
Tag segments where multiple speakers talk simultaneously.
Confidence scoring
Flag uncertain turns for human review or fallback logic.
speaker attribution
deployment available
100+
languages supported
“Speaker attribution was the bottleneck in our scribe accuracy. pyannoteAI gave us reliable separation between clinicians and patients across the messy audio conditions our customers actually deal with.”