10 Ways to Improve Meeting Transcription Accuracy in Your Product

Why accuracy is a trust issue, not just a quality issue

When a meeting transcript gets someone's words wrong — especially in a customer-visible context like a CRM note or a shared meeting summary — it damages trust in your product. Users who've had their words misquoted or their names misspelled in auto-generated notes stop using the feature.

Transcription accuracy for meeting audio in real-world conditions typically runs 90–97% word accuracy on English. The 3–10% of errors are distributed unevenly — they cluster around proper nouns, technical terms, names, and overlapping speech. These are exactly the words users notice most.

Here's how to push that number up.

1. Pass a language hint

This one is obvious but frequently missed. If you know the meeting language in advance, pass it:

POST /v1/bots
{
  "meeting_url": "...",
  "transcription": {
    "language": "en-GB"  // or "fr", "de", "es", "ja", etc.
  }
}

Gregnote auto-detects language if you don't specify, but detection can be wrong on short segments or heavily accented speech. If your users are predominantly one language, hard-code it. If they're multilingual, let users set their preference.

2. Provide a custom vocabulary

Meeting transcripts suffer most on proper nouns — company names, product names, people's names, technical jargon. "Kubernetes" becomes "Cuba net us." "Figma" becomes "figure." "Gregnote" might come out "Greg note."

Custom vocabulary (also called a transcription hint list) provides a list of terms the model should expect and how they're spelled:

{
  "transcription": {
    "vocabulary": ["Kubernetes", "Figma", "Gregnote", "gRPC", "WebRTC", "TypeScript"]
  }
}

The model boosts the probability of these terms appearing in the output. Effect size: 40–70% reduction in proper noun errors.

3. Encourage better recording hardware

The biggest accuracy gains come from better input audio, not better models. A headset microphone at 32kbps is dramatically cleaner than a laptop mic at the same bitrate.

You can't mandate hardware, but you can guide users: - Add a "recording tips" tooltip or onboarding step - Show an audio quality indicator in your UI (average volume, noise floor) - Remind users before important meetings that mic choice affects transcript quality

The education investment is low; the accuracy payoff is high.

4. Handle multi-speaker overlap in post-processing

Overlapping speech is where transcription and diarization degrade most. Two people talking simultaneously produces an audio segment that the model has to handle as a single stream.

You can't fix the underlying audio, but you can post-process the transcript to flag these segments:

// Flag short speaker changes as potential overlap
function flagOverlap(segments: Segment[]): Segment[] {
  return segments.map((seg, i) => {
    const prev = segments[i - 1];
    if (prev && seg.speaker !== prev.speaker) {
      const gap = seg.start - prev.end;
      if (gap < 0.1) {  // < 100ms gap = likely overlap
        return { ...seg, confidence: "low", overlap: true };
      }
    }
    return seg;
  });
}

Surface these low-confidence segments differently in your UI — a lighter colour, an italicised style, a tooltip explaining that this segment may be less accurate.

5. Disfluency filtering

Transcripts of natural speech include disfluencies: "um," "uh," "you know," "like," "sort of," and false starts ("I was going to — actually let me rephrase that"). These are accurate transcriptions of what was said, but they make the transcript much harder to read.

Post-process to remove or collapse common disfluencies:

const DISFLUENCIES = /(um+|uh+|er+|hmm+|mhm|you know|like|sort of|kind of|right)/gi;

function cleanTranscript(text: string): string { return text .replace(DISFLUENCIES, "") .replace(/s{2,}/g, " ") .trim(); } ```

Offer this as an option, not a default — some users (lawyers, journalists) need the verbatim transcript. Most product users want the clean version.

6. Segment length affects accuracy

Very short speaker segments (under 3 seconds) are transcribed less accurately than longer segments. The model doesn't have enough context.

If your downstream use case doesn't require word-level granularity, consider merging short segments from the same speaker:

function mergeShortSegments(segments: Segment[], minDuration = 3): Segment[] {
  const result: Segment[] = [];
  for (const seg of segments) {
    const prev = result[result.length - 1];
    if (prev && prev.speaker === seg.speaker && (seg.start - prev.end) < 0.5) {
      prev.text += " " + seg.text;
      prev.end = seg.end;
    } else {
      result.push({ ...seg });
    }
  }
  return result;
}

7. Confidence scores: surface uncertainty

Many transcription APIs return confidence scores per segment or per word. Don't hide these from users — surface them:

// Show low-confidence words with a lighter colour in your transcript viewer
function renderWord(word: Word) {
  const opacity = word.confidence > 0.8 ? 1 : word.confidence > 0.6 ? 0.7 : 0.4;
  return `<span style="opacity: ${opacity}">${word.text}</span>`;
}

Users who see that a name was transcribed with 40% confidence will correct it. Users who see a confident-looking transcript assume it's correct and propagate the error.

8. Allow inline corrections

The fastest way to improve transcript quality over time is to let users correct errors directly in the transcript view. Store corrections and use them to build product-specific vocabulary.

A simple correction model: - User clicks on a word in the transcript - Inline text input appears - User types the correct word and saves - Correction stored against the (meeting_id, word_id, original_text, corrected_text)

Aggregate corrections across users to discover systematic errors (your product name getting transcribed wrong in 40% of meetings) and feed them back as vocabulary hints.

9. Model selection for domain-specific use cases

General speech recognition models are trained on broad audio data. If your use case is domain-specific (medical, legal, financial, technical software), fine-tuned or domain-adapted models often perform 10–30% better on the vocabulary of that domain.

If you're using Gregnote for a specialised use case, ask about domain-specific optimisation. The vocabulary hint approach (tip 2) covers most cases, but for high-accuracy requirements a fine-tuned model makes a significant difference.

10. Test with your actual users' audio

Don't test transcription accuracy with clean, clear recordings. Test with the actual audio conditions your users encounter: laptop mics, coffee shop background noise, Indian English accents, fast talkers, technical jargon.

Create a test set of 20–30 representative meeting recordings, transcribe them, and manually score accuracy. Measure again after each change. Accuracy work without measurement is guesswork.

Related reading

For how speaker attribution works and how to get better results from diarization specifically, see speaker diarization explained. For building the full transcript pipeline in your product, see building meeting intelligence into your SaaS product.