---
title: "How should I evaluate OpenAI transcription when getting names right matters more than fluent text?"  
description: "How should I evaluate OpenAI transcription when getting names right matters more than fluent text?"  
author: "Shamma Al Marzooqi"  
published: 2026-10-17  
updated: 2026-10-17  
canonical: https://www.mindstick.com/forum/162531/how-should-i-evaluate-openai-transcription-when-getting-names-right-matters-more-than-fluent-text  
category: "Speech Recognition"  
tags: ["OpenAI", "speech recognition", "AI evaluation"]  
reading_time: 1 minute  

---

# How should I evaluate OpenAI transcription when getting names right matters more than fluent text?

I’m testing OpenAI’s audio transcription for interview recordings. Most of the transcript reads well, but the mistakes that matter most are people’s names, company names, and short product codes. A smooth-looking sentence can hide a completely wrong name.

I have a small set of recordings with manually checked transcripts. They include remote calls, overlapping speech, and speakers mentioning names that sound like ordinary English words.

### What would be a fair test?

- Should I track **proper noun accuracy** separately from the overall word error rate?
- If the selected endpoint supports contextual hints, should I test with a full glossary or only names relevant to that recording?
- How can I check whether a glossary causes the model to insert an expected name when the speaker actually said something else?

I’m especially interested in how people handle genuinely unclear audio in the reference transcript. I don’t want to penalize reasonable uncertainty, but I also don’t want confident guesses counted as correct. What would you include in an **audio transcription evaluation** before using this for searchable interview archives?


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Original Source: https://www.mindstick.com/forum/162531/how-should-i-evaluate-openai-transcription-when-getting-names-right-matters-more-than-fluent-text

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