Speech recognition for different dialects and accents

China is a populous nation made up of many different ethnic groups. Specifically, due to multiple factors, dialects in different regions of China exhibit notable differences in speech characteristics, intonation, and vocabulary. As a result, research progress and practical applications in dialect speech recognition face an imbalanced situation. Therefore, exploring specific recognition methods, establishing diverse dialect corpora, and investigating regional heterogeneity within different dialects are crucial for enhancing the accuracy and applicability of Chinese speech recognition. This paper sorts out the key technologies related to dialect speech model, integrates the deep neural network and Supervised learning of the model. In addition, data enhancement and adaptation methods of various model techniques, attention mechanism, end to end System are also introduced. These techniques can effectively improve the performance of the model in different dialect environments. Moreover, the current limitations of the field will be discussed, such as the lack of accuracy in identifying certain dialects and the challenges in data collection and processing. By analyzing these issues, this research aims to propose potential solutions for the further development of dialect speech recognition technology, offering valuable reference material for researchers and developers.