Developmental research on an interactive application for language speaking practice using speech recognition technology

Oh, Eun Young; Song, Donggil

doi:10.1007/s11423-020-09910-1

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…e dynamic time regularization (DTW) algorithm was used for the first time in the analysis of speech signals to solve the problem of the unequal length of speech signals [12]. is greatly improved the efficiency and accuracy of speech recognition [13]. e techniques for extracting feature parameters gained significant progress, the linear predictive analysis started to be gradually applied, and vector quantization techniques opened a new chapter in speech recognition technology [10].…”

Section: Current Status Of Researchmentioning

confidence: 99%

The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

Han

Huwan²

2022

Security and Communication Networks

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To improve the accuracy of English pronunciation level evaluation, we study the modularization of the English pronunciation level evaluation system unfolding based on machine learning. The S3C2440A chip is used as the main processor of the system, and the spoken English recordings are sent to the evaluation module through the speech upload module. In the evaluation module, the pronunciation signal is filtered by the multilayer wavelet feature scale transformation method, and the intonation, speed, pitch, rhythm, and emotion features are decomposed and extracted. The test results show that the misjudgment rate of different mispronunciations is less than 1% when the system is used to evaluate the English pronunciation level, which proves that it has high evaluation accuracy. In-depth study of speech recognition related theories, speech scoring, and pronunciation correction algorithms are discussed, and an assisted learning system based on AP scoring method and pronunciation resonance peak comparison technology is proposed for the problem of inaccurate pronunciation scoring and lack of effective feedback of speech recognition technology applied to oral learning. The English pronunciation training system has achieved the expected pronunciation following of English phonetic symbols and words, real-time pronunciation. The English pronunciation training system has achieved all basic functions such as pronunciation following, real-time pronunciation evaluation, and pronunciation correction of English phonemes and works as expected. After testing, the system has achieved high accuracy in pronunciation scoring, and the similarity with experts’ scoring is over 90% for vowel and word pronunciation; the efficiency of pronunciation correction reaches 80%, which can improve learners’ pronunciation level to a certain extent.

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Section: Current Status Of Researchmentioning

confidence: 99%

The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

Han

Huwan²

2022

Security and Communication Networks

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“…For example, language learning class time is limited, so students cannot spend much time on communication and drills using the target language. In addition, teachers who need to assess the speaking abilities of learners and offer feedback are usually not able to provide feedback to each individual learner given high instructor-student ratios (Ehsani & Knodt, 1998;Oh & Song, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Scholars suggest that this issue can be addressed by using speech recognition technology (SRT) (Ehsani & Knodt, 1998;Oh & Song, 2021;Shadiev, Hwang, Chen & Huang, 2014;Shadiev, Hwang, Huang & Liu, 2016). In the process of speech recognition, SRT receives the speaker's verbal input, analyzes it, and then generates an output, for example, in the form of a text (McKechnie et al, 2018;Radha & Vimala, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In the process of speech recognition, SRT receives the speaker's verbal input, analyzes it, and then generates an output, for example, in the form of a text (McKechnie et al, 2018;Radha & Vimala, 2012). In the past two decades, SRT has made remarkable progress (Oh & Song, 2021;Shadiev, Wang, Wu & Huang, 2021;Xiao & Park, 2021). Major advances made during this period include the maturity and continuous improvement of the hidden Markov modeling (HMM) approach, which became the mainstream SRT; more attention has also been given to research on knowledge-based SRT and to artificial neural networks in SRT.…”

Section: Introductionmentioning

confidence: 99%

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Review of research on applications of speech recognition technology to assist language learning

Shadiev¹,

Liu²

2022

ReCALL

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Speech recognition technology (SRT) is now widely used in education because of its potential to aid learning, particularly language learning. Nevertheless, SRT has received only limited attention in earlier review studies. The present research aimed to address this gap in the field. To this end, 26 articles published in SSCI journals between 2014 and 2020 were selected and reviewed with respect to domain and skills, technology and their application, participants and duration, measures, reported results, and advantages and disadvantages of SRT. The results showed that English received much more attention than any other language, and scholars mostly focused on facilitating pronunciation skills. Dragon Naturally Speaking and Google speech recognition were the most popular technologies, and their most frequent application was providing feedback. According to the results, college students were involved in research more than any other group, most studies were carried out for less than one month, and most scholars administered a questionnaire or pre-/posttest to collect the data. Positive results related to gains in proficiency and student perceptions of SRT were identified. The study revealed that improved affective factors and enhanced language skills were advantages, whereas a low accuracy rate and insufficiency (i.e. lack of some useful features to support learning efficiently) of SRT were disadvantages. Based on the results, the study puts forward several implications and suggestions for educators and researchers in the field.

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“…As a consequence, this research aimed to fill a gap regarding the application of mobile technology instead of CALL in language acquisition. On the other hand, many researchers, including Allagui (2019); Chen et al (2020); Alobaid (2020); ; Oh and Song (2021); Eutsler (2021), had included mobile technology into their researches such as creative writing, writing and vocabulary. After finding a research gap, this study attempted to fill it by examining spelling acquisition.…”

Section: Introductionmentioning

confidence: 99%

Did you Run the Telegram? Use of Mobile Spelling Checker on Academic Writing

Wong¹,

Muhammad²,

Chuah³

et al. 2022

MAJESS

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Technology in education has a pivotal role in language learning in this era of globalization. This study aims to determine the impact of mobile spelling checker on academic writing among preuniversity students of a college in Malaysia. Two objectives are formed. The primary objective is to evaluate the result of the mobile spelling checker on academic writing produced by Malaysian pre-university students. Additionally, another objective is to investigate their perceptions regarding the mobile spelling checker utilised. The quantitative research approach was used. A survey questionnaire was employed to explore their perceptions and satisfaction about the spelling checker used. Cluster random sampling was employed. The samples consisted of 99 preuniversity students. The results provide light on the proper usage of mobile spelling checker for academic writing. The mobile spelling checker receives many favourable responses. The research concludes that the mobile spelling checker can meet the diverse demands of pre-university students in addition to the linguistic requirements of core courses and industry.

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Developmental research on an interactive application for language speaking practice using speech recognition technology

Cited by 14 publications

References 42 publications

The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

Review of research on applications of speech recognition technology to assist language learning

Did you Run the Telegram? Use of Mobile Spelling Checker on Academic Writing

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