English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm

Abstract

With the economic globalization continuous growth of China’s socioeconomic level tends to be internationalized, China’s attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students’ English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model.

Application of Computer-Aided Technology in Teaching Spoken English

“…Computer-aided technology has revolutionized the landscape of English language learning, offering an array of innovative tools and resources that cater to the diverse needs and preferences of learners [6]. These technological advancements have democratized access to high-quality language education, breaking down geographical barriers and providing learners with opportunities for self-directed study and personalized learning experiences [7].…”

“…Zhang (2022) presents a model for an English speech recognition system leveraging computer-aided functions and neural network algorithms, potentially enhancing accuracy and efficiency Liu and Yu (2022). focus on optimizing models for computer-aided English teaching within local area networks, aiming to improve effectiveness and performance Nguyen (2022).…”

Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English

“…Speech recognition technology has become an integral component of spoken English teaching in recent years, offering innovative ways to enhance language learning [10]. By leveraging advanced algorithms and machine learning, educators can provide personalized feedback to students on pronunciation, intonation, and fluency in real time.…”

Retracted: English Speech Recognition System Model Based on Computer‐Aided Function and Neural Network Algorithm

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”