Abstract
This chapter investigates the influence of visual corrective feedback (henceforth VCF) techniques on enhancing pronunciation accuracy among ESL learners (N = 40) from various countries and L1 backgrounds. Utilizing a mixed-methods approach, the research examines the efficacy of various VCF modalities, such as interactive software: Praat and YouGlish, in improving learners' pronunciation skills. Quantitative analysis involves pre- and post-assessment of pronunciation accuracy using standardized metrics. At the same time, qualitative data is gathered through learner interviews to gauge perceptions and experiences with VCF methods. The findings suggest a significant correlation between the use of VCF and enhanced vowel production accuracy. Additionally, the qualitative insights reveal positive learner attitudes towards VCF tools, highlighting their motivational and corrective influences on pronunciation improvement. This study offers several theoretical and pedagogical implications.
Top1. Introduction
In the realm of second language acquisition (SLA), the importance of corrective feedback (CF) for rectifying learners' linguistic errors has been widely acknowledged as a crucial factor in the successful learning of a second language (L2), especially in L2 accurate pronunciation. L2 pronunciation accuracy remains a critical dimension of language acquisition, influencing effective communication and overall proficiency (Nguyen & Hung, 2021). Over the last 30 years, research has highlighted the significance of CF in language learning (Ellis et al., 2008; Liakina & Liakin, 2023; Lyster & Ranta, 1997; Mahmood, 2023). In this sense, CF can be seen as one of the methods of providing more input to L2 learners, previous scholars emphasized the role of input in L2 improvement. One type of input is through auditory or visual aids (Alahmadi, 2019; Ellis, 2015; VanPatten et al., 2004). The auditory and visual elements of language input play crucial roles in the development of accurate pronunciation skills (Derwing & Munro, 2005). In the past four decades, significant technological progress has given rise to various speech analysis tools. Among these, is the visual corrective feedback (VCF), also known as electronic visual feedback. The typical current VCF setup involves (a) a nonnative speaker recording the stimuli; (b) a visual representation of speech features, typically focusing on intonation contours; (c) a visual display comparing the nonnative speaker's production with that of a native speaker, often accompanied by corresponding auditory feedback; and (d) the nonnative participant re-recording in an attempt to replicate native-speaker productions (Olson, 2014; Olson, 2022; Olson & Offerman, 2021).
In recent years, there has been a notable focus on providing CF in L2 pronunciation teaching and instruction, responding to the heightened interest in L2 pronunciation (Lyster et al., 2013; Mahmood, 2023; Saito & Lyster, 2012). Various methods of CF provision, including explicit and implicit approaches, have been employed. With the advancement of technology, particularly the integration of computer-based programs, L2 pronunciation teaching has taken a significant leap forward. The use of learning technology, especially automatic speech recognition (ASR), presents a multitude of new possibilities for language pronunciation training (Chen, 2011; Hao-Jan Chen et al., 2024; Hsieh et al., 2023). The increasing speed of computers and the integration of multimedia enable the creation of more interactive and personalized learning environments, promising to enhance the traditional classroom model of language learning (Bashori et al., 2022).
Moreover, studies have explored the potential application of computer tools in developing a computer-assisted language learning system (CALL) (Wang & Young, 2012) and have assessed the effectiveness of CF within the system for learners across different age groups (Chau & Bui, 2023; Chen, 2022; Nazir et al., 2023; O’Brien et al., 2018). These investigations consistently indicated that learners, both adults and young learners, exhibited improved English pronunciation with detailed CF. However, limited research has delved into a specific type of pronunciation feedback – visual feedback – comprehensively.
Key Terms in this Chapter
Oral Corrective Feedback: Oral corrective feedback is a form of feedback provided in language learning contexts, specifically in response to spoken language. It involves correcting errors or providing guidance on pronunciation.
Visual Feedback: Visual feedback refers to information or cues provided through visual means, such as images, waveforms, spectrograms, or any other visual representation. It can be used to enhance learning such as accurate production of sounds in pronunciation teaching.
Automated Speech Recognition (ASR): Automated Speech Recognition is a technology that converts spoken language into written text using computer algorithms and machine learning. ASR systems are commonly used in applications like voice recognition software, virtual assistants, and language learning platforms to transcribe spoken words accurately.
Pronunciation Instruction: Pronunciation instruction involves teaching and guiding individuals on how to articulate sounds, stress patterns, and intonation in a particular language. It is a component of language education aimed at improving learners' spoken language skills and ensuring effective communication.
Computer-Assisted Pronunciation Training (CAPT): Computer-Assisted Pronunciation Training refers to the use of technology, particularly computer programs or applications, to assist individuals in improving their pronunciation skills in a targeted language. CAPT often includes features like ASR for real-time feedback on pronunciation.
L2 Sound Production: It refers to the articulation of speech sounds in a second language acquired after the first language. It involves the reproduction of phonemes, intonation patterns, and other aspects of pronunciation specific to the target language.
Educational Technology: Educational technology, often abbreviated as EdTech, encompasses the use of technology tools, resources, and digital platforms to enhance and support teaching and learning processes.