How Notah Measures Arabic Transcription Accuracy

Introduction

Arabic transcription quality should not be judged by one generic percentage. Real accuracy depends on dialect, audio quality, code-switching, and whether the output is usable after the meeting.

What we believe should be measured

Why one headline number is not enough

For example, a model may perform well on clear Modern Standard Arabic but drop significantly in:

• Saudi or Gulf business speech
• Meetings with overlapping speakers
• Calls with browser or laptop audio
• Conversations that switch between Arabic and English

A practical evaluation model

What teams should compare

1. Recognition quality

Does the transcript preserve the meaning of the meeting, or does it force heavy manual correction?

2. Decision and task quality

Can your team identify what was decided and what happens next?

3. Retrieval quality

Can you find the right moment later without replaying the entire recording?

Suggested benchmark scenarios

Dialect sensitivity: Evaluate a representative sample with your team; no measured score is published here.

Mixed-language usefulness: Evaluate a representative sample with your team; no measured score is published here.

Post-meeting actionability: Evaluate a representative sample with your team; no measured score is published here.

Conclusion

Useful Arabic transcription is about more than a single score. The right benchmark looks at dialect realism, audio realism, and whether the output helps the team move faster after the meeting.

Notah is built for that practical standard: usable Arabic-first meeting notes, not just isolated transcription demos.