Technology company Google announced the release of Gemini 3.1 Flash Text-to-Speech (TTS), a new-generation speech synthesis model designed to improve controllability, expressiveness, and output quality for developers, enterprises, and end users building AI-driven audio applications.
The rollout of Gemini 3.1 Flash TTS is currently underway across multiple Google platforms. The model is available in preview for developers through the Gemini API and Google AI Studio, while enterprise users can access it in preview via Vertex AI. Integration is also being introduced for Google Workspace users through Google Vids, expanding the model’s availability across consumer and professional environments.
The updated system represents an advancement in synthetic voice generation, with Google reporting measurable improvements in naturalness and expressive capability. According to independent benchmarking by Artificial Analysis, which evaluates large-scale human preference data for speech models, Gemini 3.1 Flash TTS achieved an Elo score of 1,211. The same evaluation places the model within a high-performance category combining strong speech quality with comparatively efficient cost characteristics. The system also supports more than 70 languages and includes multi-speaker dialogue functionality, alongside fine-grained control options driven by natural language inputs.
Expanded Controls And Creative Direction For Speech Generation
A key feature of the release is the introduction of audio tags, a mechanism that allows users to guide speech output more precisely by embedding structured instructions directly into text prompts. These controls enable adjustments to pacing, tone, and vocal style within a single generation workflow. The system also supports layered direction, allowing developers to define scene context, assign speaker roles through configurable audio profiles, and modify delivery attributes at both global and sentence level.
Within enterprise environments using Vertex AI, these controls are intended to support more advanced production use cases, including scalable voice generation for applications requiring consistent character voices or dynamic dialogue systems. The integration also includes export functionality, allowing generated configurations to be converted into API-ready formats for deployment across different platforms and services.
The model has been positioned as suitable for global-scale deployment, with consistent performance across more than 70 languages. This multilingual capability is combined with enhanced prosody control, enabling more localized and natural-sounding speech outputs across different linguistic contexts.
Early testing feedback from developers and enterprise users has indicated increased precision in voice design and greater flexibility in shaping expressive output. The use of audio tags has been highlighted as a significant addition for constructing more complex spoken interactions, particularly in scenarios requiring character-driven or narrative-based audio generation.
All audio output generated through Gemini 3.1 Flash TTS is embedded with SynthID watermarking technology. This system introduces an imperceptible identifier within generated audio content, enabling detection of AI-generated media and supporting efforts to improve content authenticity and mitigate misuse risks.
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Source: Mpost.io
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