Google AI Studio Launches Gemini 3.1 Flash Text-to-Speech with Tag Control for Speed and Accent
Google AI Studio has launched the Gemini 3.1 Flash text-to-speech model, allowing users to insert [tags] before dialogue text to directly control speed, accent, emotional expression, and delivery style, such as [whispers], [laughs], [slow], or [enthusiasm], achieving more natural voice generation.
Developers can iterate and test multi-speaker dialogues and tag effects in the Composer view, and then export code with one click for application development. The model supports over 70 languages, and all generated audio is embedded with SynthID watermarks to identify AI content.
Source: Public Information
ABAB AI Insight
Google's update marks a shift in text-to-speech technology from basic synthesis to a programmable performance tool. Traditional TTS primarily relies on overall prompts, while Gemini 3.1 Flash introduces over 200 audio tags, allowing precise intervention in expression, rhythm, and emotion at the sentence level. This directly lowers the barrier for developing professional voiceovers and voice agents, shifting control from the model's default behavior to user instructions.
From a technological substitution perspective, this capability accelerates the industrial migration in content production. Software, games, virtual assistants, and educational tools can quickly generate multilingual, emotionally varied speech, reducing costs in labor-intensive recording processes, but also amplifying dependence on high-quality training data. In terms of wealth distribution, beneficiaries are mainly developers and platforms that master prompt engineering and integration capabilities, while traditional voice talent faces further replacement pressure.
In the long-term structural change, this embeds the tension between AI-driven productivity enhancement and distribution mechanisms. Tag control enhances personalized expression but relies on centralized model infrastructure, reinforcing Google's pricing power and data closed-loop in voice generation. Historical experience shows that each refinement of similar tools pushes content consumption from standardization to fragmentation and multimodality, while testing institutional adaptability in intellectual property, deepfake recognition, and labor transformation, driving capital concentration towards ecosystems that can efficiently utilize such interfaces.