Language Cognition and Pronunciation Training Using Applications

Abstract

In language learning, adults seem to be superior in their ability to memorize knowledge of new languages and have better learning strategies, experiences, and intelligence to be able to integrate new knowledge. However, unless one learns pronunciation in childhood, it is almost impossible to reach a native-level accent. In this research, we take the difficulties of learning tonal pronunciation in Mandarin as an example and analyze the difficulties of tone learning and the deficiencies of general learning methods using the cognitive load theory. With the tasks designed commensurate with the learner’s perception ability based on perception experiments and small-step learning, the perception training app is more effective for improving the tone pronunciation ability compared to existing apps with voice analysis function. Furthermore, the learning effect was greatly improved by optimizing the app interface and operation procedures. However, as a result of the combination of pronunciation practice and perception training, pronunciation practice with insufficient feedback could lead to pronunciation errors. Therefore, we also studied pronunciation practice using machine learning and aimed to train the model for the pronunciation task design instead of classification. We used voices designed as training data and trained a model for pronunciation training, and demonstrated that supporting pronunciation practice with machine learning is practicable.

An Investigation Into Artificial Intelligence Speech Evaluation Programs With Automatic Feedback for Developing EFL Learners’ Speaking Skills

“…The AI speech evaluation program was found beneficial for second or foreign language learners’ development of speaking skills (Dai & Wu, 2023; Dizon, 2020; Wang & Young, 2015; Xiao & Park, 2021). It is also perceived to be interesting, user-friendly and beneficial for learners’ improvement in fluency, pronunciation, and intonation (Bahi & Necibi, 2020; Ding et al, 2019; Kan & Ito, 2020; Tai & Chen, 2023). Studies also found that AI speech evaluation programs can reduce learners’ speaking anxiety (Bashori et al, 2022a, 2022b; Chiu, 2013; Tai & Chen, 2023).…”

Potential Limitations and Effective Integration of Mobile Pronunciation Tools for English Language Learners

“…These tools provide learners with access to authentic pronunciation models, allowing them to listen to and imitate native speakers. According to Kan and Ito (2020), their research on language learning and pronunciation training using applications demonstrated the effectiveness of mobile tools in providing individualized practice and immediate feedback, thereby helping learners identify and correct pronunciation errors.…”

“…The findings in the literature review are in line with earlier studies on learning language and mobile pronunciation tools. Studies that highlight the potential benefits of personalized practice, timely feedback, and access to real pronunciation models suggest the potential of mobile pronunciation tools to improve pronunciation skills (Kan & Ito, 2020). Research demonstrating the significance of learner engagement in language learning is aligned with the usage of gamification aspects, interactive features, and progress tracking in mobile technologies to encourage engagement and motivation (Yürük, 2020).…”

Production of Mandarin Chinese Tones by Sri Lankan CFL Learners: An Acoustic Analysis

“…Xing (2006, p. 99) proposed that since the knowledge of the Chinese sound system is useful for Chinese language students, they should become familiar with it along with Chinese characters. Kan and Ito (2020) investigated MC tones and proposed that auditory cognitive ability in the second language is an essential factor in the perception and production of pronunciation (p. 12). They also emphasized the importance of polysyllable perceptual training and tone comparison.…”