Google DeepMind Launches Gemini 3.1 Flash TTS with Audio Tags for Fine-Grained Voice Control
Key Takeaways
- ▸Audio Tags enable granular control over vocal characteristics—style, delivery, and pace—through text-based commands, making TTS outputs more customizable than previous versions
- ▸Support for 70+ languages demonstrates broad global accessibility, with particular emphasis on non-English languages like Hindi, Japanese, and German
- ▸SynthID watermarking on all outputs provides authentication and helps address concerns about AI-generated audio being used deceptively
Summary
Google DeepMind has unveiled Gemini 3.1 Flash TTS, an advanced text-to-speech model that introduces Audio Tags—a new feature enabling users to control vocal style, delivery, and pace directly through text commands. The model represents a significant step forward in natural-sounding speech synthesis, supporting over 70 languages including Hindi, Japanese, and German.
The new TTS model is being rolled out across multiple platforms: developers can access a preview via the Gemini API and Google AI Studio, enterprise customers will receive early access through Vertex AI, and the general public will gain access through Google Vids. All outputs include SynthID watermarking technology, Google's synthetic media authentication system designed to detect AI-generated audio.
This release highlights Google's commitment to making AI-powered speech synthesis more controllable and accessible to both developers and end users, while addressing concerns around authenticity through built-in watermarking.
- Multi-tier rollout strategy—API preview, Vertex AI enterprise access, and Google Vids public availability—ensures broad developer and user adoption
Editorial Opinion
Gemini 3.1 Flash TTS represents a meaningful leap in making AI-powered voice generation both more controllable and more responsible. The introduction of Audio Tags addresses a key friction point in TTS workflows—the difficulty of achieving specific vocal characteristics without extensive experimentation. However, the effectiveness of SynthID watermarking as a safeguard against misuse will ultimately depend on widespread adoption across the ecosystem and user awareness of its presence.
