Google DeepMind Releases Gemini 3.1 Flash TTS Featuring Granular Audio Tag Controls
The latest release of Gemini 3.1 Flash TTS focuses on enhancing the emotional range and technical precision of synthetic speech. By introducing granular audio tags, the model allows developers to direct specific nuances in audio generation, moving beyond static text-to-speech outputs toward more dynamic and lifelike interactions. This update represents a shift toward more steerable AI assets that can better serve complex customer service or storytelling applications.
Related tools
Recommended tools for this topic
These picks prioritize high-intent tools relevant to this topic. Some links may include partner or affiliate tracking.
Strong fit for AI, backend, and frontend readers looking for an AI-first coding workflow.
View CursorNatural next step for readers evaluating LLM adoption, APIs, and production inference.
Explore APIA strong fit for readers comparing Claude-class models, safety, and long-context workflows.
View AnthropicComparison
| Aspect | Before / Alternative | After / This |
|---|---|---|
| Control Granularity | Limited control over tone and pacing using standard SSML tags | Granular audio tags for precise direction of expressive nuances |
| Expressivity Range | Static and often monotonous synthetic voice profiles | High-fidelity expressive speech with varied emotional output |
| Developer Interface | Basic text-to-audio conversion with fixed parameters | Directable speech generation using sophisticated tagging systems |
Action Checklist
- Identify existing audio workflows for potential integration Evaluate which applications require the highest levels of expressive speech
- Map current SSML implementations to new granular audio tags Ensure compatibility with existing text-to-speech logic
- Conduct side-by-side quality evaluations of output audio Compare previous generation TTS with the new Flash TTS outputs
- Implement a staged rollout to minimize production risk Start with non-critical services before full deployment
Source: DeepMind Blog
This page summarizes the original source. Check the source for full details.