Abstract: With the rapid development of speech recognition technology,dialect speech recognition has become significant in va-rious application scenarios.To address challenges such as phonetic variations,speech speed differences,and noise interference in dialect recognition,this paper proposes a dialect speech recognition method based on the CTC-Conformer model,aiming to improve recognition accuracy and robustness for dialect speech.The model combines the Conformer architecture and CTC mechanism.The encoder uses convolutional neural networks and multi-head self-attention mechanisms to extract local features and long-range dependencies from audio,enhancing the understanding of dialect speech.The decoder adopts the CTC mechanism and a dual-attention mechanism,reducing the need for alignment and enhancing contextual modeling ability.A multi-task learning stra-tegy optimizes the balance between CTC loss and cross-entropy loss,further improving recognition accuracy.Experimental results show that the proposed CTC-Conformer model achieves a 79.08% character accuracy on standard test sets,and it maintains stable perfor-mance in noisy environments,with an accuracy of 65.20% even in severe noise conditions,demonstrating its robustness and precision.In summary,the proposedCTC-Conformer model provides an efficient and robust solution for dialect speech recognition,with broad potential for real-world applications.

Key words: Speech recognition, Dialect recognition, Attention mechanism, CTC-Conformer, Data augmentation