Google has launched the Gemini 3.5 Transcribe speech-to-text model, combining speech recognition with semantic-level text editing for real-time dictation and pre-recorded audio transcription.

The model can remove filler words like "um" and "uh," recognize speaker self-corrections, and output organized text; users can provide a custom vocabulary to improve accuracy in handling technical terms, order numbers, postal codes, and other alphanumeric entities. This capability means the output is not a natural verbatim record but a semantically processed text. blog+1

In the FLEURS multilingual benchmark tests for major languages and regions, the streaming transcription word error rate for Gemini 3.5 Transcribe was 5.50%, while the non-streaming rate was 5.04%; Google claims that compared to the previous generation Chirp 3, the final transcription generation time has been reduced by 70%. Another set of average scores measured by Artificial Analysis showed streaming at 4.0% and non-streaming at 2.6%, with different evaluation methods and datasets used. blog+1

The model can automatically recognize and transcribe over 85 languages, handling different regional accents and dialects; pre-recorded audio supports timestamp attribution for up to three speakers, while recognition for more than three speakers is still in experimental stages. Google also offers Transcribe Live for real-time interaction and Transcribe API for pre-recorded audio processing. deepmind+1

This model has been implemented through the Rambler feature of the Android version of Gboard and is available for public preview in Google AI Studio and Antigravity via the Gemini API; the Gemini Enterprise Agent Platform is also in public preview, with Chrome support listed as a future plan. Public reports indicate that Rambler is currently only launched in select countries and languages, and not all Pixel or Android users have access yet. theverge+1

In terms of market mechanisms, Google embeds transcription capabilities into Gboard, Gemini, and enterprise APIs, benefiting developers and enterprise clients who need to handle cross-language customer service recordings, meeting minutes, and voice input; lower latency and reduced error rates may shift some demand from standalone transcription software to Google's models and cloud interfaces. Users whose core business relies on verbatim legal evidence, medical records, or news quotes face risks: removing filler words and automatic corrections can enhance readability but may alter original statements, necessitating a workflow that preserves original wording. theverge+1

Source: Public Information