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          AI language model hopes to preserve metropolis' local dialect

          By ZHENG ZHENG | CHINA DAILY | Updated: 2024-11-22 07:57
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          The language model's development team discussing the project. [Photo by ZHENG ZHENG/CHINA DAILY]

          A team of researchers at Shanghai University has unveiled an artificial intelligence language model designed to help preserve and promote the native Shanghai dialect. Named Xiao Hu, the model is capable of speech recognition, text-to-speech conversion, and dialogue between the Shanghai dialect and Mandarin Chinese.

          "Our goal extends far beyond merely teaching AI to speak Shanghainese," says Zeng Jun, director of Shanghai University's publicity department. "Our true objective is to leverage this language model as a means to preserve and innovate the development of the Shanghai dialect, enabling more people, especially Shanghai, to become familiar with and use it."

          The impetus behind the project stems from the university's deep-rooted excellence in Chinese language studies and its vision of empowering Chinese through AI technology. Renowned scholars, such as Professor Qian Nairong, a leading authority on the research of the Shanghai dialect, and Associate Professor Ding Dimeng, a Shanghai dialect expert, have laid the foundation for this project.

          Ding underscored the cultural significance of the Shanghai dialect, describing it as the root of the city's identity, which contains ancient pronunciations and modern loanwords. "The Shanghai dialect is indispensable in this metropolis," Ding says. "I believe this language model can play an important role in language and dialect preservation efforts, accomplishing tasks that individuals cannot."

          The urgency of the project cannot be overstated.

          According to doctorate candidate Fan Tianyu, a key member of the development team, a recent statistic shows that only 22 percent of Shanghai residents aged 6 to 20 still use the dialect. The dialect's precarious state highlights the challenge of creating and purifying high-quality language data sets.

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