The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Method for Evaluating and Improving Vocal Pronunciation Quality of Vocal Music Students Based on Artificial Intelligence Technology
Abstract
The combination of artificial intelligence (AI) and vocal music education is a hot spot in intelligent-driven teaching practice, especially in the open and distance learning environment. In this study, the authors explore the application of AI technology in the evaluation of singing quality. By using AI as the mainstream method to analyze vocal music signals, they establish a set of quantifiable standards for the objective evaluation of vocal music performances. The results show that the scoring system based on AI can effectively evaluate the accuracy of students' singing, and the correlation coefficient between the score of the system and the score given by human experts is 0.645, with a deviation of about 3.000%. In addition, this method can identify the weak links in students' singing and provide targeted feedback, thus improving their vocal skills. This novel evaluation method improves the fairness and accuracy of performance evaluation and contributes to the overall improvement of vocal music teaching quality.
Related Content
|
Rui Nie.
© 2026.
22 pages.
|
|
Wei Li, Hui Dong, Yin Yu.
© 2026.
17 pages.
|
|
Qiaohong He.
© 2026.
14 pages.
|
|
Huiting Dai, Guanzheng Chen.
© 2026.
22 pages.
|
|
Zhuomei You, Wuwei Gong.
© 2026.
19 pages.
|
|
Bo He, Dalian Zhou.
© 2026.
18 pages.
|
|
Yuhong Ao, Wuyun Ao.
© 2026.
22 pages.
|
|
|