AI / ML
From Audio to Insight: ASR and Diarization Pipelines
By Vivek Jalondhara · 2 min read
Published Nov 2025 · Updated
Generic speech-to-text falls apart in noisy, multi-speaker rooms. Domain tuning is the whole game.
Audio documentation products have to handle overlapping voices and background noise. My Ecoscript contribution was the frontend and recording workflow; preprocessing and model evaluation are separate processing concerns.
Diarization groups speech by speaker; it does not establish a person's identity or clinical role. A speaker label and the transcript can both be wrong. Interfaces should make those limits visible and allow a reviewer to correct the associated records.
A staged processing design separates capture, preprocessing, transcription, speaker attribution and review. Keeping intermediate states observable helps explain a failure and retry the appropriate stage. The example is educational and does not establish clinical accuracy or compliance.
Represent processing stages in the interface
In an audio documentation application, the frontend needs to distinguish recording from processing and review. This state model illustrates the UI boundary; it makes no claim about the underlying model implementation.
Illustrative example · TypeScript · Simplified for explanation
type RecordingState =
| { status: "idle" }
| { status: "recording"; startedAt: number }
| { status: "processing"; recordingId: string }
| { status: "ready"; transcriptId: string }
| { status: "failed"; message: string };- Audio capture
- Processing service
- Transcript available
- Human review
Conceptual flow for the example above.
Tradeoff to keep in mind
A clear status prevents duplicate submissions and premature review. Transcription quality and speaker attribution require separate evaluation of the processing service.