According to ElevenLabs (@elevenlabsio), users can enhance the effectiveness of the Eleven v3 voice AI by following their v3 Prompting Tutorial. Key strategies include using longer prompts, which allow the AI model to generate more natural and accurate speech outputs, and selecting the most appropriate voice from the v3 Collection to better suit specific use cases. Additionally, leveraging [audio tags] provides granular control over delivery, enabling customized intonation and pacing for various business applications. These best practices are designed to improve voice cloning quality and expand the range of practical applications such as AI-driven customer service, content creation, and voice-over production. By continuously experimenting with these techniques, businesses can achieve more engaging and human-like voice AI experiences, opening new market opportunities in automated audio content and digital communications (source: ElevenLabs, Twitter, June 12, 2025).

According to ElevenLabs (@elevenlabsio), the new Eleven v3 (alpha) model is now the most expressive AI Text to Speech solution, introducing multi-speaker dialogue with advanced contextual awareness. This update expands language support from 33 to over 70, significantly increasing global accessibility for businesses deploying AI voice solutions. Additionally, v3 supports audio tags such as [excited], [sighs], [laughing], and [whispers], enabling more nuanced and natural voice synthesis for industries like entertainment, education, and customer service seeking to leverage hyper-realistic AI voices for multilingual, context-rich audio applications (Source: ElevenLabs Twitter, June 12, 2025).

According to @OpenAI, audio tags such as [sarcastic], [whispers], [excited], or [strong French accent] are powerful tools to fine-tune AI voice delivery in generative models. These tags allow developers to select voices that match the intended emotional tone and context, resulting in more engaging and relevant user interactions. For businesses, this enables the creation of personalized customer experiences, dynamic virtual assistants, and differentiated audio branding strategies. However, the effectiveness of each tag depends on the voice model's capabilities, as not all voices can convincingly portray every tag. This presents opportunities for AI vendors to develop more versatile voice models and for enterprises to leverage tailored audio delivery for customer engagement, as highlighted by OpenAI's recent developer documentation (source: OpenAI developer guides, 2024-05).

According to ElevenLabs (@elevenlabsio), the new Eleven v3 architecture significantly improves AI voice synthesis by deeply understanding textual input and delivering greater expressiveness. Users can now guide voice generations more accurately using audio tags that specify emotions like [sad], [angry], [happily], delivery styles such as [whispers], [shouts], and non-verbal reactions like [laughs]. This advancement enables practical business applications in content creation, entertainment, customer support, and accessibility by allowing nuanced and dynamic voice outputs, which can enhance user engagement and realism in AI-driven audio solutions (source: @elevenlabsio, June 5, 2025).