Legal speech‑to‑text built for the courtroom

AI-powered legal transcription uses automatic speech recognition (ASR) models trained on legal vocabulary to convert spoken audio into structured text in real time or from recorded files. The system processes audio through acoustic and language models, applies custom legal dictionaries, and outputs formatted transcripts with speaker labels and timestamps — ready for attorney review.

Speech-to-text can generate real-time draft transcripts during live proceedings, giving court reporters an accurate starting point to review and certify rather than transcribing from scratch. It also handles batch transcription of depositions, hearings, and evidence recordings — dramatically reducing turnaround time and backlog.

Traditional stenography relies on a trained human using a shorthand machine to capture speech at speed, then translate and format the transcript manually. AI transcription captures audio directly and produces a structured draft in seconds. The key difference is speed and scalability — AI handles volume that would require multiple stenographers, though a certified reporter still reviews and certifies the final output.

Yes, when combined with human review. Leading ASR systems achieve word error rates well below 5% on legal audio with clear conditions and custom vocabularies. The workflow pairs AI-generated drafts with certified reporter review, meeting or exceeding accuracy standards while significantly reducing the time required.

For a draft transcript used as a starting point, a WER of under 3–5% is considered acceptable in most legal contexts. For the final certified transcript, the standard is effectively zero — every word must be accurate. The AI draft gets the reporter to near-perfect quickly; human review closes the remaining gap.

Audio is captured via microphone or a recording interface and streamed to the ASR engine, which returns transcript text with a latency typically under one second. The live transcript appears on screen for the reporter and, optionally, for counsel and participants. Corrections can be made in real time, and the session is simultaneously saved as a recording for post-proceeding verification.

Real-time transcription processes audio as it is spoken, delivering a live text feed during the proceeding. Batch transcription processes completed recordings after the fact — useful for depositions, archived evidence, or high-volume discovery work. Both use the same underlying models; the choice depends on whether a live transcript is operationally required.

Speaker diarization segments the audio stream into turns and assigns each segment to a distinct speaker identity. In legal settings, speakers can be pre-enrolled by voice profile so the system labels turns as "Judge," "Plaintiff Counsel," "Witness," etc., rather than generic Speaker A/B labels. This ensures every statement is correctly attributed before the reporter reviews the draft.

Yes, with voice enrollment. Participants' voice profiles are registered at the start of a case or matter, and the system maps incoming speech to those profiles throughout the proceeding. For recurring participants — judges in a particular court, for example — profiles can be stored and reused, improving attribution accuracy over time.