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The TEN Agent team recently announced that its core models **TEN Voice Activity Detection (VAD)** and **TEN Turn Detection** are now open source, providing powerful technical support for building real-time, multimodal speech AI agents. This move marks a significant advancement in the TEN framework's efforts to promote the democratization and open-source collaboration of speech interaction technology. The following is the latest information compiled by AIbase, offering an in-depth analysis of these two core models.

Recently, a speech synthesis model called Qwen-TTS has made new progress, with its latest version update completed through the Qwen API, bringing users a richer speech synthesis experience. In this update, Qwen-TTS added support for three Chinese dialects: Beijing dialect, Shanghai dialect, and Sichuan dialect, further expanding its application scenarios. The model is trained on a large-scale corpus of more than 3 million hours, achieving naturalness and expressiveness at a human level. Qwen-TTS can not only accurately

This article provides an in-depth review of the UntitledPen AI voice generation tool, analyzing its core features such as its intelligent writing assistant, lifelike voice conversion technology, and multilingual support. It helps content creators, video producers, and marketing experts evaluate the practical value and user experience of this tool.

According to Science and Technology Daily, the world's first generative artificial intelligence radiology system integrated into clinical processes developed by Northwestern University School of Medicine is revolutionizing medical imaging diagnostics. This system can identify life-threatening conditions within milliseconds, providing an innovative solution to the global shortage of radiologists. The AI system has been fully deployed in 12 hospitals affiliated with Northwestern University. In five months of actual application in 2024, the system successfully analyzed nearly 24,000 radiology reports, increasing report generation efficiency by an average of 15.5%, boosting work efficiency for some doctors.

Significant progress has been made in the field of AI voice technology as Fish Audio announces the open sourcing of its new Text-to-Speech (TTS) model, OpenAudio S1-Mini. As a streamlined version of the highly-acclaimed S1 model, S1-Mini has triggered industry discussions due to its lightweight design, high expressiveness, and multi-language support. Key Features: Lightweight and High Performance OpenAudio S1-Mini is a lightweight version distilled from the 4B-parameter S1 model, containing only 0.5B parameters.

Global leading AI voice technology company, ElevenLabs, has officially released its latest text-to-speech model, Eleven v3 (Alpha version). It is considered the most expressive AI voice model to date. This breakthrough not only enhances the naturalness and emotional expression of speech synthesis but also provides content creators and developers with more powerful tools to assist in video, audiobook, and multimedia tool development. Technical Breakthrough: More Natural Dialogue and Emotional Expression. Eleven v3 introduces a new

With the rapid development of artificial intelligence technology, the text-to-speech (TTS) field has reached a new milestone. On June 5, 2025, ElevenLabs officially launched its latest text-to-speech model, Eleven v3 (Alpha version), known as the 'Strongest' TTS model. This model can not only convert text into natural and fluent speech but also simulate tone changes and non-verbal expressions in real conversations through precise emotional control and multi-language support, providing creators and developers with unprecedented voice generation capabilities.

Recently, Qwen released CoGenAV, innovating speech recognition technology with the concept of audio-visual synchronization. It effectively addresses the challenge of noise interference in speech recognition. Traditional speech recognition performs poorly in noisy environments, while CoGenAV takes a different approach by learning the temporal alignment relationships among audio-visual-text, building a more robust and generalizable speech representation framework, systematically improving tasks such as speech recognition (VSR/AVSR), speech reconstruction (AVSS/AVSE), and audio-visual synchronization (A').