ASR-based dictation practice for second language pronunciation improvement

McCrocklin, Shannon

doi:10.1075/jslp.16034.mcc

Cited by 39 publications

(45 citation statements)

References 27 publications

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“…To date, the few studies that have investigated ASR practice on segmental learning have shown that although ASR can be beneficial for pronunciation improvement, the effects depend on the target L2 sounds and the participants' L1 (Chen et al, 2020;Guskaroska, 2019;Liakin et al, 2015;McCrocklin, 2019). The current study adds support to this claim by providing evidence that ASR dictation practice was beneficial for the /i/-/ɪ/ contrast, but not for /ɛ/-/ae/.…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

ASR for EFL Pronunciation Practice: Segmental Development and Learners’ Beliefs

Inceoglu¹,

Lim²,

Chen³

2020

The Journal of AsiaTEFL

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The current study explored the usefulness of mobile-based automatic speech recognition (ASR) pronunciation practice by investigating a) its effects on the production of four English vowels, and b) learners' perception of ASR as a learning tool. A total of 19 Korean university students produced 28 minimal pair sentences containing the English vowel contrasts /i/-/ɪ/ and /ɛ/-/ae/ (e.g., I said beat, I said bit) at pretest and posttest, and completed six sessions of ASR practice outside of class that involved voice-typing a short text, minimal pairs in sentences, and decontextualized minimal pairs. Results of acoustic analysis of F1 and F2 formant frequencies showed a meaningful improvement in frontness for the vowel /i/, but no changes for the other vowels. Overall, the majority of the participants perceived ASR as useful for pronunciation practice, but some showed skepticism and frustration regarding the current state of the technology. Further discussed are the problems and limitations that EFL learners experienced during the ASR training.

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Section: General Discussion and Conclusionmentioning

confidence: 99%

ASR for EFL Pronunciation Practice: Segmental Development and Learners’ Beliefs

Inceoglu¹,

Lim²,

Chen³

2020

The Journal of AsiaTEFL

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“…These dictation ASR lack built-in functions for language learning, and were developed mostly for native speakers’ communication. However, they do not require a paid subscription and are therefore more accessible for learners, teachers, institutions, and researchers (S. McCrocklin, 2019b). This accessibility allows their developers to significantly increase speech recognition accuracy through a collection of vast speech databases.…”

Section: Research Backgroundmentioning

confidence: 99%

“…More importantly, such fast and automatic feedback prevents the development of incorrect pronunciation habits (Eskenazi, 1999). Furthermore, implementation of ASR software is reported to be rewarding and fun (Purushotma, 2005), enjoyable and effective for pronunciation skills development (Liakin et al., 2017; S. McCrocklin, 2019a; Mroz, 2018). It leads to pronunciation improvement among young and adult English as a Second Language (ESL) learners (e.g., Hincks, 2005; S. M. McCrocklin, 2016).…”

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confidence: 99%

Effects of Automatic Speech Recognition Software on Pronunciation for Adults With Different Learning Styles

Evers

Chen

2020

Journal of Educational Computing Research

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This study investigated how learning styles (visual/verbal) and the use of Automatic Speech Recognition (ASR) software affect English as a Second Language adult learners’ improvement during a 12-week course focusing on pronunciation. In the control group (n = 28), the teacher corrected and gave feedback on the adult learners’ pronunciation; experimental group 1 (n = 33) used dictation ASR along with peers’ correction; and experimental group 2 (n = 31) used dictation ASR alone. Their pre- and post-tests on pronunciation in reading tasks and live conversation were analyzed with their learning styles taken into account, using 2-way ANCOVA. The results suggest that learning styles made a significant difference in the pronunciation performance of the reading task in all groups. Visual style learners outperformed verbal style learners in the reading task. The combination of ASR and peer correction yielded the highest improvement in both reading tasks and live conversation.

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“…Providing a sense of human intelligibility (Mroz, 2018), the transcripts can be utilized to identify probable errors at the word and sound level (McCrocklin, 2019b) and detect error patterns (McCrocklin, 2019c; Wallace, 2016). Further, learners using dictation have improved not only in segmental accuracy (Liakin, Cardoso, & Liakina, 2014; McCrocklin, 2019a), but also in overall intelligibility (Mroz, 2020). Finally, learners introduced to ASR dictation practice report greater motivation and autonomy (McCrocklin, 2016; Mroz 2018).…”

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confidence: 99%

Revisiting Popular Speech Recognition Software for ESL Speech

McCrocklin

Edalatishams

2020

TESOL Quarterly

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Early interest in dictation programs for second language (L2) pronunciation learning emerged following rapid advancement of automatic speech recognition (ASR) and increased availability of commercial programs in the 1980s and 1990s (Rabiner & Juang, 2008). Dictation programs, which utilize ASR to provide text of users' speech, were not created for nonnative speakers (Cucchiarini & Strik, 2018), but researchers grew interested in whether transcripts could provide individualized feedback for learners (Coniam, 1999; Derwing, Munro, & Carbonaro, 2000). The usefulness of dictation depends upon the accuracy of the transcript and whether mistranscriptions are due to pronunciation errors. Ideally, a program would recognize speech as humans would, with mistranscriptions resulting from pronunciation errors that also reduce human listener intelligibility (Derwing et al., 2000). Twenty years ago, Coniam (1999) and Derwing et al. (2000) examined the accuracy of a popular dictation program, Dragon Naturally Speaking, for nonnative English speech. At the time, Dragon was a frontrunner in its field and dominated the late 1990s market (Pinola, 2011). Coniam (1999) asked 20 participants (10 first language English, 10 first language Chinese) to read two passages to the program after training it to their pronunciation, finding substantial differences in accuracy between native and nonnative speech. Derwing et al. (2000) asked 30 participants (10 first language English, 10 first language Spanish, and 10 first language Chinese) to dictate 60 sentences to the program, while audio recording. The recordings were played for 41 native-speaking listeners who transcribed and rated the speech samples on accentedness and comprehensibility. Expert raters also marked each sentence for segmental errors. Dragon transcribed less accurately than human listeners, particularly for nonnative speech. While

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ASR-based dictation practice for second language pronunciation improvement

Cited by 39 publications

References 27 publications

ASR for EFL Pronunciation Practice: Segmental Development and Learners’ Beliefs

ASR for EFL Pronunciation Practice: Segmental Development and Learners’ Beliefs

Effects of Automatic Speech Recognition Software on Pronunciation for Adults With Different Learning Styles

Revisiting Popular Speech Recognition Software for ESL Speech

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