A multimedia network English listening teaching model based on confidence learning algorithm of speech recognition

2021 ◽  
pp. 002072092098467
Author(s):  
Yubing Yao ◽  
Congying Ma
Keyword(s):  
Speech Recognition ◽  
Posterior Probability ◽  
Learning Algorithm ◽  
Confidence Score ◽  
Posteriori Probability ◽  
Support Vector ◽  
Teaching Model ◽  
Network Technology ◽  
Model Based ◽  
Multimedia Network

Language is the most important communication tool of human beings, and listening is one of the basic skills of language expression. Without good listening comprehension ability, it is impossible to use language flexibly to communicate. Due to the influence of traditional education mode, Chinese students' English listening is generally poor. Therefore, a new English listening teaching mode is needed to help students improve their English listening. In this paper, multimedia network technology is used to realize the integrated English listening teaching of listening, speaking and dictation skills, and a multimedia network English listening teaching model based on speech recognition confidence learning algorithm is proposed. In order to improve the effectiveness of the mainstream confidence method based on Lattice posterior probability, this paper proposes an improved confidence algorithm based on Lattice posterior probability, and then converts the obtained confidence score into discriminant confidence score by Support Vector Machine(SVM) to further enhance the discriminant ability of confidence. Aiming at the imbalance of training data, a score correction strategy is proposed. The experiment shows that the English listening teaching model realized by using multimedia network technology can effectively enhance the students interest in learning and improve their listening ability. And the improvement of the mainstream confidence method based on Lattice posteriori probability can effectively improve the recognition ability of the algorithm and further improve the students’ English listening learning effect.

scholarly journals A Spoken English Teaching System Based on Speech Recognition and Machine Learning

2021 ◽  
Vol 16 (14) ◽  
pp. 68
Author(s):  
Fengming Jiao ◽  
Jiao Song ◽  
Xin Zhao ◽  
Ping Zhao ◽  
Ru Wang
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Error Detection ◽  
English Learners ◽  
Teaching Method ◽  
Learning Algorithm ◽  
Support Vector ◽  
Teaching System ◽  
English Teaching ◽  
Spoken English

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.

scholarly journals An Exploration of English Teaching for Mechanical Engineering from the View of Meta-cognitive Strategy Based on Multimedia Network

2016 ◽  
Vol 11 (02) ◽  
pp. 43 ◽  
Author(s):  
Wenting Ma
Keyword(s):  
Language Learning ◽  
Mechanical Engineering ◽  
Engineering Students ◽  
Learning Ability ◽  
Computer Assisted ◽  
Teaching Model ◽  
Model Based ◽  
Experimental Teaching ◽  
Experimental Type ◽  
Multimedia Network

To improve the efficiency and effect of mechanical English learning, the advantages of computer-assisted learning is analyzed, and the teaching model based on Multimedia network is proposed. By means of comparing the results of the experimental teaching model, conclusions are drawn that the experimental type (ET) can stimulate students’ motivation much more than the control type (CP) can do. To elaborate the theoretical foundations of both task-based language teaching (TBLT) and computer assisted language learning (CALL) for mechanical engineering students, it shows the necessity and feasibility of TBLT based on Multimedia- Network for mechanical English. A teaching model for mechanical English is constructed which includes an experimental research in its application in teaching mechanical engineering students. The result of the experiment proves that the task-based teaching model based on multimedia network changes the students’ passive attitude in traditional teaching process and makes them more active in the class. In this case students’ interest and creativity are stimulated and their autonomous learning ability can be trained.

scholarly journals Multimedia Network English Reading Teaching Model Based on Speech Recognition Confidence Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9 ◽  
Author(s):  
Chuanju Wang
Keyword(s):  
Speech Recognition ◽  
Learning Algorithm ◽  
Principal Component ◽  
Learning System ◽  
Svm Classifier ◽  
Communication Tool ◽  
English Teaching ◽  
English Reading ◽  
Multimedia Network ◽  
Reading Teaching

With deepening internationalization, English has become an increasingly important communication tool. Because traditional English teaching has short teacher-student interaction time, lack of oral English training environment, and single learning method, the oral English teaching is not ideal, and the students’ “speaking” confidence is insufficient. Aimed at addressing the exposed problems of traditional English reading teaching, this paper proposes a multimedia-based English reading teaching mode. On this basis, establish a voice recognition phoneme network grid to detect the recognition results. Secondly, the lattice is used to generate the confusion network mesh, and the acoustic posterior probability is calculated. Then, the feature vector is input into the SVM classifier for confidence mark, and finally the feature is extracted by principal component analysis. The research shows that multimedia network teaching can teach more vividly, increasing the initiative of students. At the same time, it is shown that the speech recognition confidence learning algorithm can improve the language learning system.

Intelligent model for speech recognition based on SVM: A case study on English language

2020 ◽  
pp. 1-11
Author(s):  
Qian Hou ◽  
Cuijuan Li ◽  
Min Kang ◽  
Xin Zhao
Keyword(s):  
Speech Recognition ◽  
English Language ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Recognition System ◽  
Spectral Subtraction ◽  
Spectral Amplitude ◽  
Support Vector ◽  
Linear Classifiers ◽  
Intelligent Technology

English feature recognition has a certain influence on the development of English intelligent technology. In particular, the speech recognition technology has the problem of accuracy when performing English feature recognition. In order to improve the English feature recognition effect, this study takes the intelligent learning algorithm as the system algorithm and combines support vector machines to construct an English feature recognition system and uses linear classifiers and nonlinear classifiers to complete the relevant work of subjective recognition. Moreover, spectral subtraction is introduced in the front end of feature extraction, and the spectral amplitude of the noise-free signal is subtracted from the spectral amplitude of the noise to obtain the spectral amplitude of the pure signal. By taking advantage of the insensitivity of speech to the phase, the phase angle information before spectral subtraction is directly used to reconstruct the signal after spectral subtraction to obtain the denoised speech. In addition, this study uses a nonlinear power function that simulates the hearing characteristics of the human ear to extract the features of the denoised speech signal and combines the English features to expand the recognition. Finally, this study analyzes the performance of the algorithm proposed in this study through comparative experiments. The research results show that the algorithm in this paper has a certain effect.

Build a "Computer Network Technology" Teaching Model Based on Work Process

2010 ◽  
Vol 44-47 ◽  
pp. 3138-3142
Author(s):  
Xiao Ming Bi ◽  
Chun Ru Xiong ◽  
Qian Sheng Zheng
Keyword(s):  
Computer Network ◽  
Teaching Model ◽  
Network Technology ◽  
Work Process ◽  
Work Task ◽  
Model Based ◽  
Process Oriented ◽  
Technology Teaching ◽  
Technology Course

This paper analyzes deficiencies in the course teaching of computer network technology. Combined with the characteristics of computer network-related jobs. this paper builds a Work-process oriented, Work-task Derived “Computer network technology” course teaching model.

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