Google introduced Gemini 3.5 Transcribe for batch and live speech recognition

Google introduced Gemini 3.5 Transcribe on August 26 as its latest speech-to-text model family, in public preview for developers through the Gemini API and Google AI Studio and for enterprises through Gemini Enterprise Agent Platform. The release post covers Transcribe and Transcribe Live, distinguishing stored-audio processing from real-time transcription. Google positions the models for precise, context-aware transcripts rather than simple word conversion. A DeepMind model card published the same date lists limitations including possible hallucinations, slowness and timeouts.

Transcription output is derived data

A transcript can be searched and acted on more easily than audio, which also makes an error more likely to propagate. Names, numbers, negation and speaker attribution deserve separate confidence checks when downstream systems create tickets, summaries or records. Real-time captions add partial hypotheses that may change as later audio arrives.

Applications should preserve time offsets and a link to the relevant source segment so reviewers can recover the original evidence. Do not let a fluent transcript become the only record when the audio is authoritative and retention rules allow it.

Evaluate the whole audio pipeline

Test microphone encoding, resampling, chunk boundaries and network interruption as well as the model. Build an evaluation set with accents, domain terms, cross-talk, background media, silence and code-switching. Measure deletion, insertion, speaker and timestamp errors according to the product’s actual use.

For live use, specify whether interim text can trigger automation or only final text may do so. Add a visible correction path and mark machine-produced transcripts. Google’s launch expands available speech processing, but reliable use depends on preventing uncertain text from silently becoming a trusted system of record.