Phonetic Feature Detection and Comparative Analysis of Chinese Dialects Using Computer-Based Methods

Computer-aided dialect speech recognition is an important interdisciplinary field combining linguistics and computer science. The phonological complexity of Chinese dialects poses major challenges for traditional analysis methods. While computational approaches have gained traction, issues like data scarcity and poor model generalization remain. This paper reviews current techniques and proposes a new deep learning approach enhanced with transfer learning to address these limitations. Experimental results show improved accuracy and robustness over conventional methods. A comparative analysis highlights key advancements and future research directions. This work contributes to both linguistic and information sciences and supports efforts in documenting endangered dialects.