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The system extracts high-level performance techniques from existing vocal datasets using transfer learning, and fine-tunes the pre-trained ResNet-50 convolutional neural network to cater to the specific needs of vocal training. A genetic algorithm is then employed to optimize the training plan, which is continuously refined through multiple iterations to meet individual student needs. Personalized training plans are provided based on the students\u2019 voice characteristics and learning requirements. The system\u2019s performance was evaluated, showing significant improvements in training effectiveness, with a training improvement score of 4.5 \u00b1 0.4 and a user satisfaction score of 4.7 \u00b1 0.3. The system also achieved a personalization score of 4.8 \u00b1 0.2 and reduced training time to 28 \u00b1 3\u00a0min. 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