Google has launched Gemini 3.1 Flash TTSA text-to-speech preview model that focuses on improving speech quality, expressive control, and multilingualism. Unlike previous iterations that prioritized easy conversion, this release emphasizes natural language audio tags, native support for more than 70 languages, and native multi-speaker chat.
This release marks the transition from ‘black box’ audio generation to a message-based workflow. The model is being released in preview with Gemini API and Google AI Studio, with Vertex AI for businesses, and with Google Vids for Workspace users.
Speech Quality, Control, and Developer Workflow
The outstanding technical achievement of Gemini 3.1 Flash TTS is its performance on industry benchmarks. The model currently reports i Artificial Analysis of TTS for the leading Elo score of 1,211it sets it as Google’s most natural and sound speech model to date.
Beyond raw quality, the update introduces a sophisticated control layer for AI developers. Instead of relying on static configuration, developers can now use audio tags and natural language information to direct the following:
- Style and Tone: Instructs the model to change delivery based on local context.
- Shipping and delivery: Adjusting the rhythm and emphasis of speech to suit specific narrative needs.
- Accent and language: Using local nuances within 70+ supported languages.
Multi-Native Speaker Conversation
The key difference of Gemini 3.1 Flash TTS is its support multi-speaker dialogue. Traditional TTS pipelines often require separate API calls for different voices, which can lead to inconsistent compatibility. By handling multiple speakers natively, the model maintains a more natural conversational flow, making it particularly useful for developers creating podcasts, feature scripts, or assistant interfaces.
Security and Identification: SynthID Watermarking
As audio production reaches higher levels of fidelity, the ability to identify AI-generated content becomes a technological necessity. Google is included SynthID watermarking for all audio generated by Gemini 3.1 Flash TTS.
The implementation of SynthID is built around two key elements:
- Invisibility: The watermark is embedded in a way that does not degrade the listener’s experience of the sound.
- Reliable Detection: The watermark enables the identification of AI-generated content, helping to prevent misinformation and ensuring transparency in digital ecosystems.
Technical Summary
| A feature | Clarification |
| Model | Gemini 3.1 Flash TTS (Preview) |
| Elo Score | 1,211 (TTS analysis leaderboard) |
| Language Support | 70+ Languages |
| Important features | Audio tags, Natural language control, multi-speaker dialogue |
| Safety | Integrated SynthID Watermarking |
| Platforms | Gemini API, AI Studio, Vertex AI, Google Vids |
Overall, Gemini 3.1 Flash TTS represents a move towards an ‘authoritative’ AI audio approach. By combining high-level benchmark performance with granular natural language controls, the Google AI team provides tools to build voice experiences that feel as small as integrated output and more like guided operations.
Check out Technical details, Developer preview available now on Gemini API and Google AI StudioFor companies previewed in Vertex AIand Workplace Users with Google Vids . Also, feel free to follow us Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to Our newspaper. Wait! are you on telegram? now you can join us on telegram too.
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Michal Sutter is a data science expert with a Master of Science in Data Science from the University of Padova. With a strong foundation in statistical analysis, machine learning, and data engineering, Michal excels at turning complex data sets into actionable insights.