Introducing Precision-3: 10.4% lower diarization error, new sensitivity parameters, frame-level speaker probabilities, and speed-to-accuracy operating modes.

Transcription accuracy is not what breaks production Voice AI. Speaker diarization, identification, and turn dynamics are the layer most pipelines are missing.

Why speaker counting is the hardest part of diarization, how clustering-based pipelines estimate it, and how to bypass the failure modes with a single parameter.

How do you handle overlapping speakers in transcription? Detect overlap at the diarization layer first, then transcribe separated per-speaker streams.

Should you self-host speaker diarization or use an API? A sourced breakdown of GPU cost, engineering tax, accuracy gap, and when each path actually makes sense.

Why transcription APIs keep getting speaker labels wrong: the four failure modes that follow from labeling speakers after words, and the fix that doesn't switch vendors.

What is the best speaker diarization tool in 2026? A sourced comparison of pyannote, AssemblyAI, Deepgram, NeMo, SpeechBrain, Kaldi, and Speechmatics.

Should you use pyannote or SpeechBrain for speaker diarization? A sourced look at what each project is built for and where the two make different choices.

pyannote vs. NVIDIA NeMo Sortformer: how the two open diarization stacks compare on accuracy, speaker limits, setup, and production readiness.