Your research is only as good as your transcript

Transcript Shield cleans your interview transcripts so your findings are worth defending.

The work nobody talks about

Automatic Speech Recognition (ASR) models have made it easy to generate transcripts. Getting one you can actually trust is still the hard part.

5-10 hours

of manual cleanup for every
1 hour of recording hour.

Sponholz et al., 2025

5-10 hours

of manual cleanup for every 1 hour of recording hour.

Sponholz et al., 2025

5x

more errors on multi-speaker audio versus single-speaker.

Deepgram, 2025

35% / 19%

35% error rate for Black speakers compared to 19% for White speakers.

Koenecke et al., 2025

5x

more errors on multi-speaker audio versus single-speaker.

Deepgram, 2025

35% / 19%

35% error rate for Black speakers compared to 19% for White speakers.

Koenecke et al., 2025

5-10 hours

of manual cleanup for every 1 hour of recording hour.

Sponholz et al., 2025

5x

more errors on multi-speaker audio versus single-speaker.

Deepgram, 2025

35% / 19%

35% error rate for Black speakers compared to 19% for White speakers.

Koenecke et al., 2025

Transcript Shield focuses on the four tenets of a clean transcript

Fidelity

Fixing what the AI model misheard. This is where the custom dictionary lives to capture product names, acronyms, etc.

Identiy & Labeling

Fixing speaker diarization and misheard names.

Readability

Removing filler words withot over-correcting valuable silence and long hesitations.

Privacy

Redacting the transcript before ever hitting an AI model.

Fidelity

Fixing what the AI model misheard. This is where the custom dictionary lives to capture product names, acronyms, etc.

Identiy & Labeling

Fixing speaker diarization and misheard names.

Readability

Removing filler words withot over-correcting valuable silence and long hesitations.

Privacy

Redacting the transcript before ever hitting an AI model.

Embed it into your existing workflows