Year: 2025
Type: article
Abstract: Wu dialects carry deep regional culture, but due to significant internal variations, automated recognition faces considerable challenges. This study focuses on speech recognition and semantic feedback for Wu dialects, proposing a deep learning system with regional adaptability and semantic feedback capabilities. First, a corpus covering multiple Wu dialect regions (WXDPC) is constructed, and a two-level phoneme mapping and regional difference modeling mechanism is introduced. By incorporating geographical region labels and transfer learning, the model’s performance in non-central regions is improved. Experimental results show that as the training corpus increases, the model’s CER significantly decreases. After the introduction of regional labels, the CER in non-central Wu dialect regions decreased by 4.5%, demonstrating the model’s effectiveness in complex dialect environments. This system provides technical support for the preservation and application of Wu dialects and offers valuable experience for the promotion of other dialect recognition systems.
Source: Applied Sciences
DOI: 10.3390/app151810227
Authors: Xinyi Yue, Lizhi Miao, Jiahao Ding
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414349469