Assessment in second language pronunciation

Kang, Okim; Kermad, Alyssa

doi:10.4324/9781315145006-32

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…1 As the accuracy or accent of pronunciation is evaluated as a deviation from a targetlike benchmark, it is necessary to define targetlike pronunciation for the assessment of pronunciation accuracy. However, due to the fact that there are different models of L2 pronunciation learning, especially in English, the assessment of pronunciation entails a substantive difficulty in defining what constitutes targetlike pronunciation (Kang & Ginther, 2018). Given the potential challenge to the validity of pronunciation accuracy, we thus decided not to include a cognitive fluency measure for pronunciation accuracy.…”

Section: Articulatory Skillsmentioning

confidence: 99%

The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency

Suzuki

Kormos

2022

Stud Second Lang Acquis

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The current study examined the extent to which cognitive fluency (CF) contributes to utterance fluency (UF) at the level of constructs. A total of 128 Japanese-speaking learners of English completed four speaking tasks—argumentative task, picture narrative task, reading-to-speaking task, and reading-while-listening-to-speaking task—and a battery of linguistic knowledge tests, capturing vocabulary size, lexical retrieval speed, sentence construction skills, grammaticality judgments, and articulatory speed. Their speaking performance was analyzed in terms of speed, breakdown, and repair fluency (i.e., UF), and scores on linguistic knowledge tests were used to assess students’ L2 linguistic resources and processing skills (i.e., CF). Structural equation modeling revealed a complex interplay between the multidimensionality of CF and UF and speaking task types. L2 processing speed consistently contributed to all aspects of UF across speaking tasks, whereas the role of linguistic resources in speed and repair fluency varied, depending on task characteristics.

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Section: Articulatory Skillsmentioning

confidence: 99%

The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency

Suzuki

Kormos

2022

Stud Second Lang Acquis

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“…Spoken language learning can be accomplished through diverse methods, including formal instruction, self-study, immersion, interaction, and technology. One of the approaches to integrating technologies into spoken language learning is computer-assisted pronunciation training (CAPT), a subfield of computer-assisted language training (CALL) [2,3]. It mainly involves the assessment of pronunciation errors and the detection and correction of prosody errors.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Applying Large Language Models to Spoken Language Learning

Gao,

Nuchged,

et al. 2023

Applied Sciences

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People have long desired intelligent conversational systems that can provide assistance in practical scenarios. The latest advancements in large language models (LLMs) are making significant strides toward turning this aspiration into a tangible reality. LLMs are believed to hold the most potential and value in education, especially in the creation of AI-driven virtual teachers that facilitate language learning. This study focuses on assessing the effectiveness of LLMs within the educational domain, specifically in the areas of spoken language learning, which encompass phonetics, phonology, and second language acquisition. To this end, we first introduced a new multiple-choice question dataset to evaluate the effectiveness of LLMs in the aforementioned scenarios, including the understanding and application of spoken language knowledge. Moreover, we investigated the influence of various prompting techniques such as zero- and few-shot methods (prepending the question with question-answer exemplars), chain-of-thought (CoT) prompting, in-domain exemplars, and external tools. We conducted a comprehensive evaluation of popular LLMs (20 distinct models) using these methods. The experimental results showed that the task of extracting conceptual knowledge posed few challenges for these LLMs, whereas the task of application questions was relatively difficult. In addition, some widely proven effective prompting methods combined with domain-specific examples resulted in significant performance improvements compared to the zero-shot baselines. Additionally, some other preliminary experiments also demonstrated the strengths and weaknesses of different LLMs. The findings of this study can shed light on the application of LLMs to spoken language learning.

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“…Additionally, because human examiners primarily conduct the speaking assessment, the speaking test's scoring is significantly biased. Kang and Kermad [22] highlight this point as the primary concern in speaking evaluation, as the subjective nature of the calculation of the scoring process may result in rater inconsistencies or shifts, affecting the test taker's scores and, conversely, rater reliability. Therefore, the grading criteria are vital for the speaking test [23,24].…”

Section: Introductionmentioning

confidence: 99%

The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

Khan

Kumar

Benyo

et al. 2022

Education Research International

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Speaking ability is regarded as among the essential aspects of language development. Oral examination appeared challenging to evaluate due to the presence of human evaluators. The speaking method depends on the test’s reliability, determined by the raters’ scores. The current study is aimed at evaluating the speaking test’s interrater reliability utilized to measure the speaking performance of Common First Year (CFY) students during remote learning. The data were obtained from 56 EFL learners using a scoring sheet and rubrics. Eight raters were responsible for rating the study. The speaking test’s reliability was estimated using quantitative data analysis. Correlation coefficients and the Bland-Altman test were employed to assess raters’ agreement. SPSS was utilized to analyze the data in this investigation. The study’s findings suggested that the speaking exam used in the CFY program during remote learning has shown some reliability on correlations and acceptable norms on the Bland-Altman test.

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Assessment in second language pronunciation

Cited by 7 publications

References 1 publication

The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency

The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency

An Investigation of Applying Large Language Models to Spoken Language Learning

The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment

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