From Audio to Insight: ASR and Diarization Pipelines
Generic speech-to-text falls apart in noisy, multi-speaker rooms. Domain tuning is the whole game.
On ECHOSCRIPT, the first lesson was that off-the-shelf transcription collapses in a real dental clinic — overlapping voices, instrument noise, and rapid exchanges destroy accuracy. Audio preprocessing, segmentation, and noise reduction had to come before any model.
Diarization — knowing who said what — matters as much as the words. ML-driven speaker separation, reinforced with contextual reasoning, assigns clinical and operational roles so the transcript is actually trustworthy.
The pipeline is staged and observable: capture, clean, segment, transcribe, diarize, then reason. Each stage is independently testable, which is the only way to debug an ML system you can trust in healthcare.
Vivek Jalondhara
Full Stack Software Engineer