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Speaker diarization
Automatically identify distinct speakers in every conversation, with a labeled transcript.
Speech-to-text
built for
the real world Speech-to-text built for
the real world
While other transcription services were built for clean audio, Plaud's transcription pipeline is optimized for messy, offline environments where conversations actually happen.
Hardware + software
advantage
Accuracy is driven from a combination of superior audio quality from the devices plus a robust pre-processing pipeline & our own diarization model.
01
Directional Microphones
Multi-directional microphones capture speech for better diarization, while beamforming focuses on active speakers.
02
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On-Device Algorithms
VPU runs real-time signal processing to clean audio for better downstream transcription accuracy.
03
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Pre-processing & Denoising
Spectral subtraction and adaptive filtering strip background noise, HVAC, crowd chatter before any upload.
04
Transcription API
Transcription API intakes audio and metadata to deliver high accuracy, speaker-diarized transcripts in JSON.
01
Directional Microphones
Multi-directional microphones capture speech for better diarization, while beamforming focuses on active speakers.
02
On-Device Algorithms
VPU runs real-time signal processing to clean audio for better downstream transcription accuracy.
03
Pre-processing & Denoising
Spectral subtraction and adaptive filtering strip background noise, HVAC, crowd chatter before any upload.
04
Transcription API
Transcription API intakes audio and metadata to deliver high accuracy, speaker-diarized transcripts in JSON.
What you get back
112 languages
Support multiple languages in a single conversation, with published accuracy benchmarks for each language.
Real numbers.
Real environments.
These aren't benchmark lab numbers. They come from testing in the same conditions your users work in.
A public benchmark run by Mozilla, used across the industry to measure transcription accuracy.
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