Gemini 3.1 Flash TTS release: granular audio tags for speech

DeepMind has released Gemini 3.1 Flash TTS, a new text-to-speech model that introduces granular audio tags to give developers fine control over expressive speech. The model is presented as an evolution in AI audio generation, enabling direction of intonation, timing, emphasis and emotional tone within a single synthesized voice.

Gemini 3.1 Flash TTS embeds tag-based controls into the generation pipeline so producers can specify audio-level attributes inline with the text. DeepMind positions the tags as a mechanism to move beyond static voice profiles, allowing dynamic shifts such as changes in pitch on a word, micro-pauses between phrases, or an emotional inflection for a sentence without swapping models.

How the granular audio tags work

The model accepts standard text input augmented with discrete audio tags. Tags are designed to represent parameters that influence prosody and voice quality, including pitch contours, timing adjustments, emphasis markers and broad emotional states. Developers can mix tags inside a single utterance, and the model generates a continuous audio waveform that reflects those instructions.

DeepMind describes the approach as enabling precise, time-aligned control. That means a producer can target a single syllable for a pitch rise, lengthen a pause after a clause, or chain multiple expressive directives over a paragraph. The tags operate at a finer resolution than profile-level toggles and are intended to reduce the need for post-processing audio editing.

Gemini 3.1 Flash TTS also includes tooling for previewing the tagged output before final production. The preview workflow shows text alongside a visual prosody map to help match the desired timing and emphasis. DeepMind says the system is optimized to keep inference latency low enough for interactive use cases, though exact performance numbers were not published in the initial release notes.

Availability, integrations and use cases

DeepMind plans to surface Gemini 3.1 Flash TTS through developer APIs and demo interfaces. The company highlights applications across audiobooks, character voices in games, virtual assistants, accessibility tools and automated narration for short-form video. Granular tags let teams produce variations of the same script for different emotional contexts without recording additional voice talent.

The tagging approach is also intended to help quality assurance and iteration. Producers can script small tag changes to test different delivery styles, then replay or batch-generate variants for review. That can speed voice design cycles compared with recording sessions or manual editing.

Adoption will depend on how the tags are documented and how extensively the API supports cross-language prosody controls. DeepMind's release notes show examples in English and a handful of other languages, but broad multilingual behavior and edge cases for complex prosody remain to be validated by developers working at scale.

Why it matters

Granular audio tags shift control from coarse voice presets to fine-grained, programmatic direction, lowering friction for producers who need nuanced delivery. That matters for products that depend on tone and timing, such as accessibility tools, character-driven media and conversational agents. Wider adoption will hinge on documentation, latency, and how well the tags generalize across languages and expressive styles.