Audio tags
Special markers in text that control audio characteristics like tone, pitch, or emotion in synthesized speech.
Audio tags are like secret codes embedded within text. When an AI reads text with these codes, it changes how it speaks. For example, a tag might tell the AI to sound happier, sadder, or more excited.
Think of it like stage directions for an actor. The text is the script, and the audio tags are the directions telling the actor how to deliver their lines. This helps make AI voices sound more natural and expressive.
Audio tags, often embedded within text-to-speech (TTS) prompts, are special control characters or markup that instruct a synthesis model to modify specific prosodic and stylistic features of the generated audio. These tags can govern parameters such as pitch, speaking rate, volume, emphasis, and emotional tone, allowing for fine-grained control over the expressiveness of synthesized speech.
Their implementation varies, but they typically operate by mapping specific tokens or sequences to internal model states or conditioning signals during the speech generation process. This allows for dynamic adjustment of speech characteristics without requiring separate models for different styles or emotions, though the expressiveness and naturalness are dependent on the model's training and architecture.
Recent updates to Google's Gemini text-to-speech (TTS) system highlight the growing importance of audio tags. These tags allow for more nuanced and expressive control over synthesized speech, moving beyond basic pronunciation to include emotional tone and emphasis.
- graphic_eq Gemini 3.1 Flash TTSGoogle · Gemini Models ·
- graphic_eq Gemini 3.1 Flash TTSGoogle · Gemini Models ·
- Google Gemini
- Google's Gemini TTS system uses audio tags to control speech characteristics like tone, pitch, and emotion, enabling more natural and expressive voice generation.
- Phonemes
- Phonemes are the basic units of sound in a language, whereas audio tag
- SSML (Speech Synthesis Markup Language)
- SSML is a specific XML-based markup lan
- Embeddings
- Embeddings are numerical representations of words or concepts, where
The concept of using special markers to control speech synthesis has evolved alongside TTS technology. While specific implementations vary, the idea of embedding control codes within text for speech generation is a long-standing practice in the field.
If using a platform that supports custom TTS, try inputting a sentence with different audio tags. For example, experiment with tags that might indicate a question, an exclamation, or a whisper to hear how the AI's voice changes. Look for documentation on the specific TTS service you are using for available tag syntax.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- ToolGemini 3.1 Flash TTS for Low Latency SpeechGenerates natural-sounding speech with low latency and expressive control using audio tags. · 5 min
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
Questions people ask
- Can any AI voice read audio tags?
- No, only specific text-to-speech models are designed to understand and respond to audio tags. The availability and syntax of these tags depend on the AI provider.
- What kind of control do audio tags offer?
- Audio tags can control various aspects of speech, including pitch, speed, volume, emphasis, and emotional tone, making the synthesized voice sound more human-like and expressive.
First seen on Subvolts Sep 5, 2026. Updated Oct 6, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.