Qwen-TTS is developed by the Qwen team in Alibaba Cloud, and its core technology relies on the training of a very large-scale speech dataset. The dataset covers multiple languages and dialects, ensuring that the generated speech is highly natural and fluent. The system uses deep learning algorithms to automatically optimize intonation, speech rate, and emotional expression, so that the output is close to the pronunciation of a real person. Typical training data includes tens of thousands of hours of Mandarin, English, and three Chinese dialects (Beijing/Shanghai/Sichuan), and uses advanced vocoder technologies such as WaveNet to achieve fine modeling at the waveform level.
In terms of technical implementation, Qwen-TTS adopts an end-to-end neural network architecture, combined with an attention mechanism to dynamically analyze text features. For example, when dealing with dialect words such as "今儿个", the model will automatically trigger the corresponding vocalization rule base. Compared with traditional spliced TTS, its rhyming error rate is reduced by 62%, and its MOS (Mean Opinion Score) reaches 4.3 (on a 5-point scale). This quality performance makes it one of the closest TTS systems to real people's pronunciation in Chinese.
This answer comes from the articleQwen-TTS: Speech Synthesis Tool with Chinese Dialect and Bilingual SupportThe
