The acquisition of English stress by Kazakh-Russian bilinguals: The role of dominant language

Yakup, Mahire

doi:10.1121/1.5067659

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature [ 16 , 17 ] proposed a scoring mechanism based on HMM and NN technology to promote the improvement of English self-learning ability. Literature [ 18 ] pointed out the existing problems of voice scoring mechanism according to the current development situation and compared the advantages and disadvantages of various voice scoring technologies from both subjective and objective aspects. It is proposed that the scoring should be divided into three parts and the scoring mechanism should be constructed.…”

Section: Related Workmentioning

confidence: 99%

Research on Open Oral English Scoring System Based on Neural Network

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This study designs and implements a scoring system for open-spoken English using NN technology. The system scores the oral recording from the phonetic level and the text level, respectively, and can comprehensively evaluate its oral level. The system will separately score the spoken speech and the spoken content through different scoring models and add the scoring results as the final score, in which the spoken content is obtained by text transcription of the recording by an external speech recognition engine. An acoustic sensor is adopted to collect pronunciation signals of spoken English. Modern signal processing and automatic pattern recognition technology are used to distinguish the quality of spoken pronunciation. Similar semantic units are marked between acoustic feature sequences, which make use of the parallel algorithm processing mode of multi-computing cores of modern GPU and allow multiple units to independently execute the comparison algorithm at the same time. Experiments show that the model in this study achieves better comprehensive scoring performance. The scoring model is of great significance to the development of educational informatization and intelligence, and it also provides a reference for the construction of intelligent oral scoring system.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on Open Oral English Scoring System Based on Neural Network

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…technology was suggested [Biller and Johnson (8), Niggard (9)] to encourage the growth of English self-learning capacity. According to the state of development, research [Yakup (10)] identified the issues with the voice scoring mechanism and contrasted the benefits and drawbacks of different voice scoring methods from both subjective and objective viewpoints. It is suggested that the scoring be broken down into three pieces and that a scoring system be built.…”

Section: Literature Surveymentioning

confidence: 99%

An Innovative Strategy Towards Oral English Assessment Using Machine Learning, Data Mining, and Blockchain Techniques

Zhao

2023

Preprint

View full text Add to dashboard Cite

Few studies have looked at how EFL students perform while speaking in public in English, despite the fact that video data analysis is often used in applied linguistics. In a world that is becoming more globalised, effective public speaking is essential, but EFL learners struggle to execute it, and despite the significance of characteristics like eye contact and speech pauses, there is currently no objective analysis of these elements. In-depth research and analysis on the topic of adaptive proofreading of English pronunciation in a wireless sensor network setting are presented in this article. Data mining refers to the method of generating knowledge and information from a significant amount of imperfect, unpredictable, fuzzily, and randomly generated data that was unknown prior but may be beneficial. This study examines how well EFL learners do orally using video data samples and a Machine Learning (ML) strategy known as a "discrete three-layered fuzzy logic artificial neural network (DTL-FL-ANN)". The architecture of the integrity system for college students is very compatible with the decentralization, security, trustworthiness, and traceability of Blockchain Technology, which is being used in many facets of life. The spoken English data was gathered and filtered for this research using the normalization method, median filter, and "Local Contrast Stretching (LCS)" procedures. The feature extraction stage employs the multi-manifold discriminant analysis (MMDA) to extract the significant attributes from the preprocessed data. We evaluate spoken English performance using the proposed DTL-FL-ANN+BT method. In order to maximize effectiveness, DTL-FL-indicators ANN+BT's are researched and contrasted with existing approaches. The suggested methods improved performance metrics such as accuracy (95%), evaluation error (55%) and evaluation time (45 sec). The proposed approach is determined to be the most effective when compared to the effectiveness of the current techniques for oral English evaluation using machine learning and blockchain technology.

show abstract

The acquisition of English stress by Kazakh-Russian bilinguals: The role of dominant language

Cited by 2 publications

References 0 publications

Research on Open Oral English Scoring System Based on Neural Network

Research on Open Oral English Scoring System Based on Neural Network

An Innovative Strategy Towards Oral English Assessment Using Machine Learning, Data Mining, and Blockchain Techniques

Contact Info

Product

Resources

About