AI transcription for legal and deposition workflows: accuracy and privilege
Blog post from Gladia
AI transcription for legal and deposition workflows must balance low word error rates, reliable speaker diarization, confidentiality protections, and human review to produce defensible records. The material argues that general speech-to-text systems often perform poorly on noisy, overlapping, multi-speaker legal audio and recommends evaluating providers on representative deposition recordings, legal terminology, speaker attribution, data-training policies, and total feature costs rather than headline pricing or clean-audio benchmarks. It presents Solaria-3 as intended for English and European-language conversational audio and Solaria-1 for multilingual proceedings and code-switching, while describing asynchronous pyannoteAI-based diarization, word-level timestamps, structured speaker labels, and custom vocabulary support for names and citations. Because AI transcripts generally cannot replace certified court reporters for official filings in most U.S. jurisdictions, the proposed model uses AI to create a draft that trained professionals review and certify. Privacy guidance emphasizes selecting service tiers where client audio is not used for model training, promptly transferring transcripts to compliant storage, deleting source recordings, maintaining audit trails, and considering data residency, encryption, retention, and optional PII redaction.
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