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Grit is a novel, rather an important psychological variable that has been associated with performance in the English as a second or foreign language (ESL/EFL) context. However, there is a dearth of research on the relationship of grit and pronunciation performance among EFL learners. Consequently, the current study sought to establish the association between grit and the pronunciation performance of Saudi EFL learners by employing pronunciation self-efficacy beliefs as a mediating variable.

A quantitative correlational research design was deployed in this research. Data were collected from 350 Saudi university EFL learners using questionnaires and a pronunciation test.

Findings indicated that both the dimensions of grit (i.e. perseverance of effort and consistency of interest) showed a significant and positive association with the learners' pronunciation self-efficacy beliefs. Also, the pronunciation self-efficacy beliefs showed a significant and positive association with the pronunciation performance. Lastly, findings indicated that pronunciation self-efficacy beliefs mediated the association between girt (i.e. perseverance of effort and consistency of interest) and pronunciation performance.

The study’s findings have implications for the EFL classroom. By incorporating grit and self-efficacy beliefs into students, teachers can enhance their students' English pronunciation performance.

Existing research has examined English as a foreign language (EFL) students’ attitudes towards flipped learning. However, no study seems to have correlated students’ achievement and their attitudes towards flipped learning. Yemeni EFL students also face pronunciation problems and they lack technology-enhanced language learning. The flipped learning approach promotes active learning and increases students’ learning outcomes. This study aims to measure the achievement level and the attitudes of Yemeni EFL students towards flipped pronunciation classrooms. It also correlates between achievement and attitudes towards this new experience.

Using a correlational research design, quantitative and qualitative data were collected through a test, questionnaire and structured interview from an intact class of Yemeni EFL students at the College of Education in Socotra.

The one-sample t-test analysis shows that the respondents scored high in the achievement test. The analysis of both the questionnaire and the structured interview revealed that the students have a positive attitude towards the flipped pronunciation classroom. The correlation analysis indicated a high-level positive relationship between students’ achievement and their attitudes where r = 0.779 with p < 0.001, two-tailed.

The present study has some limitations regarding the sample size. This context-specific study recruited the available EFL class at the College of Education in Socotra, Hadhramout University. It would be more reliable should the study compared large groups of students so that the results can be generalized.

The study recommends using flipped learning to enhance pronunciation among EFL students.

This study contributes to the correlational studies in flipped learning.

Articulation errors substantially reduce speech intelligibility and the ease of spoken communication. Moreover, the articulation learning process that speech-language pathologists must provide is time consuming and expensive. The purpose of this paper, to facilitate the articulation learning process, is to develop a computer-aided articulation learning system to help subjects with articulation disorders.

Facial animations, including lip and tongue animations, are used to convey the manner and place of articulation to the subject. This process improves the effectiveness of articulation learning. An interactive learning system is implemented through pronunciation confusion networks (PCNs) and automatic speech recognition (ASR), which are applied to identify mispronunciations.

Speech and facial animations are effective for assisting subjects in imitating sounds and developing articulatory ability. PCNs and ASR can be used to automatically identify mispronunciations.

Future research will evaluate the clinical performance of this approach to articulation learning.

The experimental results of this study indicate that it is feasible for clinically implementing a computer-aided articulation learning system in learning articulation.

This study developed a computer-aided articulation learning system to facilitate improving speech production ability in subjects with articulation disorders.

The purpose of this paper is to present evidence of the need to have a carefully designed lexical model for speech recognition for dyslexic children reading in Bahasa Melayu (BM).

Data collection is performed to obtain the most frequent reading error patterns and the reading recordings. Design and development of the lexical model considers the errors for better recognition accuracy.

It is found that the recognition accuracy is increased to 75 percent when using context‐dependent (CD) phoneme model and phoneme refinement rule. Comparison between context‐independent phoneme models and CD phoneme model is also presented.

The most frequent errors recognized and obtained from data collection and analysis illustrate and support that phonological deficit is the major factor for reading disabilities in dyslexics.

This paper provides the first step towards materializing an automated speech recognition (ASR)‐based application to support reading for BM, which is the first language in Malaysia.

The paper contributes to the knowledge of the most frequent error patterns for dyslexic children's reading in BM and to the knowledge that a CD phoneme model together with the phoneme refinement rule can built up a more fine‐tuned lexical model for an ASR specifically for dyslexic children's reading isolated words in BM.

As the center of the fourth industrial revolution, artificial intelligence (AI) has marked its presence in various disciplines including the education field in the form of AI-powered learning applications. The purpose of this study is to build a research model capturing the relationships among use contexts, user gratification, attitude, learning performance and continuous intention to use an AI-powered English learning application.

Using the use and gratification theory, use contexts and the belief-attitude-intention theory, this paper uses a quantitative approach based on a survey method for data collection and structural equation modeling for analysis. A total of 478 students from an international university in Guangdong, China, participated in the survey after using Liulishuo for two weeks.

The results showed that perceived use contexts affected all variables associated with gratifications-obtained and gratification-opportunities. With the exception of social integrativeness, all other gratification-based factors significantly affected attitude. The attitude in turn significantly influenced learning performance and continuous use intention.

Mobile AI-powered learning applications are at the center of research on technology-enhanced learning in the age of media and technology convergence. The study is timely and contributes to the discussion of the roles of use context and gratifications on technology users’ attitudes and behavioral intentions.

Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.

DL technology is used to design a speech evaluation system.

The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.

The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

Learning Chinese is unquestionably very important and popular worldwide with the fast economic growth of China. To most foreigners and also local students, one of the major challenges in learning Chinese is to write Chinese characters in correct stroke sequences that are considered as significant in the Chinese culture. However, due to the potentially complicated structures of Chinese characters together with their stroke sequences, there are very few character recognition techniques that can effectively tackle the involved training task in an efficient and flexible manner. The purpose of this paper is to propose an intelligent and flexible e‐learning software based on learning objects to facilitate the learning of writing Chinese characters in correct stroke sequences.

The paper adopts an incremental approach in designing the overall system architecture to emphasize on extendibility of the system. The basic features of the system including the evolution and pronunciation of each Chinese character can be embedded as a part of the learning object metadata to enhance students' understanding of Chinese characters. To demonstrate the feasibility of this proposal, a prototype of the proposed e‐learning software was built on smartphones such that students can learn anytime and anywhere.

From the empirical evaluation of the e‐learning prototype for learning to write correct Chinese characters on mobile devices, it was found that foreign students can learn and practise the writing more effectively anytime and anywhere on their mobile devices after classes. Some initial positive feedback was collected. Furthermore, a more careful and thorough evaluation is planned to be conducted in relevant courses for foreign students in the upcoming Fall semester.

This proposal represents the first attempt to reduce the complexity while increasing the extendibility of the e‐learning software to learn Chinese through learning objects running on smartphones or mobile devices in general. More importantly, it opens up numerous opportunities for further investigations including possible integrations with other existing Chinese e‐learning systems.