Music classroom assistant teaching system based on intelligent speech recognition

2021 ◽  
pp. 1-10
Author(s):  
Chao Long ◽  
Shan Wang

In order to improve the effect of music classroom teaching and the degree of informatization, this paper builds a music classroom auxiliary teaching system with the support of intelligent speech recognition technology, and conducts in-depth research on the audio classification and segmentation technology of music teaching classrooms. Moreover, this paper uses support vector machines to divide audio into five types: mute, background sound, song music, speech, and noisy speech. At the same time, this paper also proposes a smoothing method based on the classification result sequence to obtain audio segmentation points. In addition, this paper constructs a system model based on the actual needs of music classroom teaching, and performs vocal feature recognition with the support of intelligent speech recognition. Finally, this paper verifies and analyzes the performance of the system constructed in this paper through experimental research. The research results show that the intelligent music classroom auxiliary teaching system constructed in this paper has a certain effect.

Author(s):  
Fengming Jiao ◽  
Jiao Song ◽  
Xin Zhao ◽  
Ping Zhao ◽  
Ru Wang

The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.


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