scholarly journals Optimization of Intelligent English Pronunciation Training System Based on Android Platform

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qianyu Cao ◽  
Hanmei Hao
Keyword(s):  
Speech Recognition ◽  
English Learners ◽  
Feedback Mechanism ◽  
Recognition Algorithm ◽  
Training System ◽  
Spoken English ◽  
Speech Recognition Technology ◽  
Depth Study ◽  
Speech Correction ◽  
Theoretical Stage

Oral English, as a language tool, is not only an important part of English learning but also an essential part. For nonnative English learners, effective and meaningful voice feedback is very important. At present, most of the traditional recognition and error correction systems for oral English training are still in the theoretical stage. At the same time, the corresponding high-end experimental prototype also has the disadvantages of large and complex system. In the speech recognition technology, the traditional speech recognition technology is not perfect in recognition ability and recognition accuracy, and it relies too much on the recognition of speech content, which is easily affected by the noise environment. Based on this, this paper will develop and design a spoken English assistant pronunciation training system based on Android smartphone platform. Based on the in-depth study and analysis of spoken English speech correction algorithm and speech feedback mechanism, this paper proposes a lip motion judgment algorithm based on ultrasonic detection, which is used to assist the traditional speech recognition algorithm in double feedback judgment. In the feedback mechanism of intelligent speech training, a double benchmark scoring mechanism is introduced to comprehensively evaluate the speech of the speech trainer and correct the speaker’s speech in time. The experimental results show that the speech accuracy of the system reaches 85%, which improves the level of oral English trainers to a certain extent.

scholarly journals Design of Students’ Spoken English Pronunciation Training System Based on Computer VB Platform

2019 ◽  
Vol 14 (06) ◽  
pp. 41
Author(s):  
Yanju Jin
Keyword(s):  
Error Correction ◽  
English Learners ◽  
Function Analysis ◽  
Training System ◽  
Scoring Method ◽  
Analysis And Design ◽  
Spoken English ◽  
English Pronunciation ◽  
Key Factor ◽  
Depth Study

Spoken English communication is most commonly used in the international communication. However, the accuracy of spoken English pronunciation is the key factor to restrict English learners in China. For the current situation that spoken English proficiency is generally low in China, this paper aims to design a spoken English pronunciation training system that will provide guidance and help for English learners’ spoken pronunciation. The Visual Basic platform is used in the design of the system. This paper first conducts an in-depth study on the related theories of voice recognition, discusses the correction algorithm of voice scoring and pronunciation, and puts forward more practical and convenient AP-based scoring method, providing full theoretical support for the design of the system. Then through the function analysis and design of the spoken English pronunciation training system, this paper realizes the system design of scoring and correcting errors of English spoken pronunciation based on the VB platform. The system boasts the basic functions, including English phonetic symbols and word pronunciation to follow, real-time voice evaluation, and pronunciation error correction. According to the test, the similarity of the system with the experts is over 90% in scoring and its efficiency of pronunciation error correction reaches 80%, which plays a certain role in improving spoken English of English learners.

Artificial intelligence speech recognition model for correcting spoken English teaching

2020 ◽  
pp. 1-12
Author(s):  
Duan Ran ◽  
Wang Yingli ◽  
Qin Haoxin
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Basic Knowledge ◽  
Correction Model ◽  
Spoken English ◽  
Speech Recognition Technology ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Important Direction ◽  
And Training

Artificial intelligence speech recognition technology is an important direction in the field of human-computer interaction. The use of speech recognition technology to assist teachers in the correction of spoken English pronunciation in teaching has certain effects and can help students without being constrained by places, time and teachers. Based on artificial intelligence speech recognition technology, this paper improves and analyzes speech recognition algorithms, and uses effective algorithms as the system algorithms of artificial intelligence models. Meanwhile, based on phoneme-level speech error correction, after introducing the basic knowledge, construction and training of acoustic models, the basic process of speech cutting, including the front-end processing of speech and the extraction of feature parameters, is elaborated. In addition, this study designed a control experiment to verify and analyze the artificial intelligence speech recognition correction model. The research results show that the method proposed in this paper has a certain 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.

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