AI Transcription Invents Words in Silence

Speech recognition can produce plausible words from audio that contains no clear speech. Check the audio segment before correcting the summary prompt: if the transcript already contains invented text, later note-taking stages may repeat it as fact.

Locate the earliest incorrect artifact

Compare the recording, raw transcript and generated notes at the same timestamp. If the transcript is wrong, focus on audio and transcription. If the transcript is accurate but the notes invent a decision, focus on summarization.

The upstream whisper.cpp silence issue reports this failure pattern. It is a user report tied to particular conditions, not an accuracy estimate for all transcription tools.

Save a short permitted sample containing the failure and enough neighboring audio to understand the boundary.

Build a three-part fixture

Use a recording you own or have permission to process:

1. A clearly spoken sentence with a known transcript.
2. A quiet but real sentence.
3. A period containing no speech.

The desired output preserves the quiet sentence and avoids adding words to the silent interval. A setting that removes both has hidden one failure by introducing another.

Keep microphone, sample rate, language and segmentation settings in the test record. Background music, overlapping speakers and abrupt cuts deserve separate fixtures.

Evaluate speech detection and segment handling

A speech-detection stage can help decide which audio should reach transcription. Its threshold needs testing against your actual recording conditions. Aggressive filtering can erase soft speech or short acknowledgments.

Inspect whether neighboring segments overlap and whether duplicated words arise during merging. Also check whether previous transcript context is influencing the current segment.

Do not copy a similarly named option between implementations without checking its documentation. Wrappers and local runtimes expose different controls; the same model family does not imply identical settings.

Preserve uncertainty downstream

If a segment remains unclear, mark it as uncertain with a timestamp. Do not convert uncertain words into an assigned task or confirmed decision.

For meeting notes, maintain a distinction between proposal, agreement and unresolved question. A participant saying “maybe Friday” should not become a firm Friday deadline merely because the summary expects a due-date field.

Require a source timestamp for important action items so a reviewer can return to the audio quickly.

Verify correction effort

Compare the number and severity of corrections across your fixtures before and after the change. Record actual review time if evaluating tools; do not infer accuracy from one clean recording.

The AI note-taking guide provides a broader comparison workflow. Use hallucination checking for the final notes, while keeping audio-level errors visible as their own category.

Related troubleshooting

• When AI Citations Do Not Support the Answer
• RAG Cannot Read a Scanned PDF: What to Check

This guide draws on the linked documentation. Examples are illustrative unless explicitly identified as measured results.