Transcribing a Lecture and Transcribing a Caller Are Not the Same Problem

Description and use

Long‑form lecture transcription and real‑time phone call transcription require distinct strategies: capture quality, pause‑based chunking, timestamp retention, structured outputs, and domain‑specific term validation.

Problem

Speech‑to‑text for long‑form lectures and real‑time phone calls face fundamentally different constraints, leading to distinct failure modes and evaluation priorities.

Outcome

By treating lecture and call transcription as separate problems, focusing on capture conditions, chunking strategy, structured extraction, and domain‑specific evaluation, the team achieved higher perceived quality and reliability without relying on model upgrades.

Workflow

• 1. Capture audio with correct sample rate and format, handling app backgrounding and screen lock.
• 2. For lectures, chunk audio around natural pauses with overlap, keep timestamps, and structure summaries with headings and definitions.
• 3. For calls, design turn‑based confirmation of identifiers, validate downstream, and build a repair ladder with exit options.
• 4. Store raw transcript, audio, and timestamps for all cases.
• 5. Evaluate using domain‑specific term accuracy rather than aggregate WER.
• 6. Tune latency settings per turn for calls, balancing endpointing aggressiveness.
• 7. Use larger models or second passes only for batch lectures where latency is not critical.
• 8. Monitor structured extraction fields for call failures, not entire calls.

Evidence

The single biggest accuracy win on the note‑taking side had nothing to do with the transcription model. It was recording conditions.

Source

Word error rate treats every word as equal. Users do not.

Source

On a phone line, you are transcribing through a straw

Source