The impact of student learning aids on deep learning and mobile platform on learning behavior

Purpose Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students' learning behavior. Design/methodology/approach DL technology is used to design a speech evaluation system. Findings The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency. Originality/value The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.