The English pronunciation of Arabic speakers: A data-driven approach to segmental error identification

Rehman, Ivana; Silpachai, Alif; Levis, John M.; Zhao, Guanlong; Gutierrez‐Osuna, Ricardo

doi:10.1177/1362168820931888

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a result was revealed by some researchers (e.g., Ababneh, 2018;Al Mafalees, 2020;Bin Hadjah & Hamzah, 2022;Farrah & Halahlah, 2020;Hamzah & Bin Hadjah, n.d.). Rehman et al (2020) assured that Arab speakers frequently struggle with voicing contrasts (e.g., /v/-/f/; /p/-/b/).…”

Section: Discussionmentioning

confidence: 99%

Production and perception of English consonants by Yemeni EFL learners

Hadjah¹,

Hamzah

2023

Indonesian J. Appl. Linguist.

View full text Add to dashboard Cite

Many previous studies on the perception-production correlation focused mainly on transcriptions and native English speaker evaluations for production accuracy assessments; only a few included acoustic measurements. The purpose of this study is to examine the production and perception of six English consonants by Yemeni EFL learners of English using an acoustic measurement of L2 production in order to better understand the link between production and perception. It has three objectives: (1) To investigate the extent to which word position influences the accuracy of Yemeni EFL learners’ production of the target consonants, (2) to examine the extent to which word position affects their perception, and (3) to examine the relationship between the overall production and perception of the investigated sounds. A quantitative research method was employed to collect data from six Yemeni EFL postgraduate students from Universiti Utara Malaysia (UUM). In the production tasks, the speakers were asked to produce seventy-two words with the target consonants three times in isolation (Experiment 1) and the other three times in a carrier sentence (Experiment 2). The researchers recorded the speakers’ productions with an iPhone and later evaluated them acoustically via Praat. In the perception test, an AXB experiment was conducted. The stimuli were read by a native male speaker of Australian English. The findings showed that word position significantly affected the production, yet not the perception of the target sounds. Moreover, an insignificant positive moderate correlation was revealed between the overall production and perception of the target consonants. Results have implications for L2 speech and pronunciation instruction and learning. Teachers may focus more on specific sound environments that lead learners to struggle while producing/perceiving particular English sounds.

show abstract

Section: Discussionmentioning

confidence: 99%

Production and perception of English consonants by Yemeni EFL learners

Hadjah¹,

Hamzah

2023

Indonesian J. Appl. Linguist.

View full text Add to dashboard Cite

show abstract

“…Mid-twentieth-century authorities attempted to justify such work by claiming that predicting phonological difficulties can improve pedagogical practices (Moulton, 1962 ). Although that opinion was disputed long ago (Walz, 1980 ), interest among teachers in error prediction has persisted (Munro, 2018 ; Rehman et al, 2020 ). Recently, Munro et al ( 2015 ) and Munro ( 2018 , 2021 ) discussed evidence that individual variability in L2 production, even among speakers of a shared L1, is greater than has sometimes been assumed.…”

Section: Introductionmentioning

confidence: 99%

Variability in L2 Vowel Production: Different Elicitation Methods Affect Individual Speakers Differently

Munro

2022

Front. Psychol.

View full text Add to dashboard Cite

Elicitation methods are known to influence second language speech production. For teachers and language assessors, awareness of such effects is essential to accurate interpretations of testing outcomes. For speech researchers, understanding why one method gives better performance than another may yield insights into how second-language phonological knowledge is acquired, stored, and retrieved. Given these concerns, this investigation compared L2 vowel intelligibility on two elicitation tasks and determined the degree to which differences generalized across vowels, vowels in context, lexical items, and individual speakers. The dependent variable was the intelligibility of Cantonese speakers' productions of English /i I u ℧/ in varying phonetic environments. In a picture-naming task, the speakers produced responses without an auditory prompt. In a second task–interrupted repetition–they heard exemplars of the same targets without pictures, and repeated each one after counting aloud to 10, a step intended to disrupt their short-term auditory store and therefore prevent simple mimicry. For target words with scores below 80% on picture naming, mean intelligibility was more than 10 points higher on interrupted repetition. However, that difference did not generalize across conditions or across speakers. Thus, although it is technically accurate to say that, on average, interrupted repetition yielded better vowel intelligibility than did picture naming, that observation requires a great deal of qualification, particularly because of individual speaker differences. The outcomes are interpreted in terms of their relevance to language assessment and phonetic learning.

show abstract

“…The synergistic effect of attitude enables students to understand the characteristics of English pronunciation from hearing, vision, and touch and improve English pronunciation. Figure 1 shows the multimodel [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor

Zhou¹,

Guo-long

2022

Journal of Sensors

View full text Add to dashboard Cite

In recent years, with the increasing frequency of international exchanges, people have gradually realized that language is a tool of communication and communication, and language learning should attach importance to oral teaching. However, in traditional classrooms, one of the problems faced by oral teaching is the mismatch of the teacher-student ratio: a teacher has to deal with dozens of students, one-on-one oral teaching and pronunciation guidance is impossible, and it is also affected by the teachers and the environment constraints. Therefore, the research on how to efficiently automate pronunciation training is becoming more and more popular. Many phonemes in English have different facial visual features, especially vowels. Almost all of them can be distinguished by the roundness and tightness of the lips in appearance. In order to give full play to the role of lip features in oral pronunciation error detection, this paper proposes a multimodal feature fusion model based on lip angle features. The model interpolates the lip features constructed based on the opening and closing angles and combines audio and video in time series. Feature alignment and fusion and feature learning and classification are realized through the two-way LSTM SOFTMAX layer, and finally, end-to-end pronunciation error detection is realized through CTC. It is verified on the GRID audio and video corpus after phoneme conversion and the self-built multimodal test set. The experimental results show that the model has a higher false pronunciation recognition rate than the traditional single-modal acoustic error detection model. The increase in error detection rate is more obvious. Verification by the audio and video corpus with white noise was added, and the proposed model has better noise immunity than the traditional acoustic model.

show abstract

The English pronunciation of Arabic speakers: A data-driven approach to segmental error identification

Cited by 5 publications

References 26 publications

Production and perception of English consonants by Yemeni EFL learners

Production and perception of English consonants by Yemeni EFL learners

Variability in L2 Vowel Production: Different Elicitation Methods Affect Individual Speakers Differently

English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor

Contact Info

Product

Resources

About