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

Abstract

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.

AI and Recognition Technologies to Facilitate English as Foreign Language Writing for Supporting Personalization and Contextualization in Authentic Contexts

“…They can go anywhere to find suitable authentic contexts related to the topics of EFL writing and then use recognition technology, that is, ITR to retrieve vocabulary related to the authentic contexts to help their writing. However, the advantages of ITR only provide students with texts (e.g., vocabulary and phrases) without any further support like a sentence making based on ITR generated texts (Shadiev, Zhang, et al, 2020). Based on the ITR texts generated from the taken photos from the real world, generating sample sentences can be realized using generative-AI.…”

“…STR is to recognize their voice for practicing English speaking (Shadiev et al, 2018). ITR is to recognize the photos from their surroundings and then retrieve related vocabulary (Shadiev, Zhang, et al, 2020). Therefore, it offers a great opportunity to bring the advantages of recognition-to-texts technologies not only for EFL speaking but also for EFL writing since it can connect authentic contexts well to support the contextualization.…”

Systematic Review (2003–2023): Exploring Technology-Supported Cross-Cultural Learning through Review Studies

“…In the future, scholars may address these issues. Shadiev and Liu (2022) [63] Unspecified Shadiev and Sintawati (2020) [50] Only twenty-five research articles were reviewed, and some important studies on intercultural learning supported by technology were omitted.…”

Potentialities of Applied Translation for Language Learning in the Era of Artificial Intelligence

“…Other examples include the use of an auto-subtitle system in a multimodal context, which has been found to improve students' language comprehension, decrease cognitive load (working memory), and increase satisfaction with the learning experience (Malakul and Park 2023). Also, Speech Recognition Technology (SRT) has been identified as a valuable tool for language learning (Shadiev and Liu 2023), offering important benefits such as a positive impact on affective factors, namely: motivation and engagement, fostering of interaction, and provision of a self-paced learning environment that accommodates diverse student needs (2023: 84).…”