Conversational audio is messy: overlap, accents, incomplete sentences, and background noise. This project needed that material prepared for structured evaluation, not only transcribed as raw text.
The problem
A production speech system cannot be judged from transcripts alone. Reviewers need speaker turns, uncertainty, and the original audio context. Without quality checkpoints, evaluation sets mix clean studio speech with noisy edge cases and then report one misleading score.
The approach
- Keep source audio, transcript, and metadata in one workspace.
- Segment conversations into turns with speaker and quality flags.
- Define evaluation criteria before labeling: accuracy, speaker attribution, and handling of unintelligible speech.
- Insert review checkpoints so difficult clips are not silently skipped.
What changed
The team received an evaluation-ready audio set: structured turns, documented edge cases, and a clear record of what had been reviewed. Model scores could be read against the same criteria used to prepare the data.
Why it matters
Audio evaluation is a data-stack problem. Source files, labels, versions, and quality status have to stay connected. When they do, production teams can see where a model fails and why.