542 research outputs found

    The development of automatic speech evaluation system for learners of English

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    ๅˆถๅบฆ:ๆ–ฐ ; ๅ ฑๅ‘Š็•ชๅท:็”ฒ3183ๅท ; ๅญฆไฝใฎ็จฎ้กž:ๅšๅฃซ(ๆ•™่‚ฒๅญฆ) ; ๆŽˆไธŽๅนดๆœˆๆ—ฅ:2010/11/30 ; ๆ—ฉๅคงๅญฆไฝ่จ˜็•ชๅท:ๆ–ฐ547

    Computer-based Assessment of Foreign Language Speaking Skills

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    In the frame of the current effort to establish an indicator of foreign language competency in Europe via the implementation of a language survey in 2011, a Conference on computer-based assessment (CBA) of speaking skills was held in Brussels in June 2010. The sponsors of this event, the Centre for Research on Lifelong Learning of the European Commission in Ispra and the Directorate General of Education and Culture, are pleased to put forward this report with the Conference proceedings. It is our hope that it can serve, not only to inform the implementation of the language survey, but also to assist Member States and their representatives in the task of gathering current evidence on the benefits and challenges linked to the implementation of CBA.JRC.DG.G.9-Econometrics and applied statistic

    Second language pronunciation assessment: A look at the present and the future

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    Over three decades ago, Michael Canale summarized what he considered to be the challenges facing language assessment in the era of communicative language learning and teaching: Just as the shift in emphasis from language form to language use has placed new demands on language teaching, so too has it placed new demands on language testing. Evaluation within a communicative approach must address, for example, new content areas such as sociolin- guistic appropriateness rules, new testing formats to permit and encour- age creative, open-ended language use, new test administration procedures to emphasize interpersonal interaction in authentic situa- tions, and new scoring procedures of a manual and judgemental nature. (Canale, 1984: 79) Applied to second language (L2) pronunciation assessment, this descrip- tion remains highly relevant today, raising a number of important issues, such as: broadening the scope of pronunciation assessment beyond the focus on a single aspect of pronunciation (e.g. segmental accuracy) or a single standard (e.g. absence of a discernible nonnative accent); targeting pronunciation assess- ment for various interlocutors in interactive settings, for instance, outside a typical focus on academic performance by students in Western societies; as well as developing and fine-tuning novel assessment instruments and proce- dures. Above all, Canaleโ€™s description aptly summarizes an ongoing quest in language assessment to capture the authenticity and interactiveness of language use (e.g. Bachman, 1990; Bachman & Palmer, 2010). The contribu- tions to this edited volume address some of the challenges identified by Canale in innovative ways. Before summarizing these contributions, we hasten to add that no edited volume, including this one, can provide an exhaustive overview of all possible issues in L2 pronunciation assessment; most chapters in this volume are focused on testing or informal evaluative judgements of speech in real-world settings and not on classroom-based assessment, including diagnostic assessment or feedback on test takersโ€™ per- formance. However, the range of topics, the variety of research methodologies and paradigms, and the scope of implications featured here make this volume a timely addition to the growing area of L2 pronunciation assessment

    Modeling statistics ITAsโ€™ speaking performances in a certification test

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    In light of the ever-increasing capability of computer technology and advancement in speech and natural language processing techniques, automated speech scoring of constructed responses is gaining popularity in many high-stakes assessment and low-stakes educational settings. Automated scoring is a highly interdisciplinary and complex subject, and there is much unknown about the strengths and weaknesses of automated speech scoring systems (Evanini & Zechner, 2020). Research in automated speech scoring has been centralized around a few proprietary systems owned by large testing companies. Consequently, existing systems only serve large-scale standardized assessment purposes. Application of automated scoring technologies in local assessment contexts is much desired but rarely realized because the systemโ€™s inner workings have remained unfamiliar to many language assessment professionals. Moreover, assumptions about the reliability of human scores, on which automated scoring systems are trained, are untenable in many local assessment situations, where a myriad of factors would work together to co-determine the human scores. These factors may include the rating design, the test takersโ€™ abilities, and the ratersโ€™ specific rating behaviors (e.g., severity/leniency, internal consistency, and application of the rating scale). In an attempt to apply automated scoring procedures to a local context, the primary purpose of this study is to develop and evaluate an appropriate automated speech scoring model for a local certification test of international teaching assistants (ITAs). To meet this goal, this study first implemented feature extraction and selection based on existing automated speech scoring technologies and the scoring rubric of the local speaking test. Then, the reliability of the human ratings was investigated based on both Classical Test Theory (CTT) and Item Response Theory (IRT) frameworks, focusing on detecting potential rater effects that could negatively impact the quality of the human scores. Finally, by experimenting and comparing a series of statistical modeling options, this study investigated the extent to which the association between the automatically extracted features and the human scores could be statistically modeled to offer a mechanism that reflects the multifaceted nature of the performance assessment in a unified statistical framework. The extensive search for the speech or linguistic features, covering the sub-domains of fluency, pronunciation, rhythm, vocabulary, grammar, content, and discourse cohesion, revealed that a small set of useful variables could be identified. A large number of features could be effectively summarized as single latent factors that showed reasonably high associations with the human scores. Reliability analysis of human scoring indicated that both inter-rater reliability and intra-rater reliability were acceptable, and through a fine-grained IRT analysis, several raters who were prone to the central tendency or randomness effects were identified. Model fit indices, model performance in prediction, and model diagnostics results in the statistical modeling indicated that the most appropriate approach to model the relationship between the features and the final human scores was a cumulative link model (CLM). In contrast, the most appropriate approach to model the relationship between the features and the ratings from the multiple raters was a cumulative link mixed model (CLMM). These models suggested that higher ability levels were significantly related to the lapse of time, faster speech with fewer disfluencies, more varied and sophisticated vocabulary, more complex syntactic structures, and fewer rater effects. Based on the modelโ€™s prediction on unseen data, the rating-level CLMM achieved an accuracy of 0.64, a Pearson correlation of 0.58, and a quadratically-weighted kappa of 0.57, as compared to the human ratings on the 3-point scale. Results from this study could be used to inform the development, design, and implementation for a prototypical automated scoring system for prospective ITAs, as well as providing empirical evidence for future scale development, rater training, and support for assessment-related instruction for the testing program and diagnostic feedback for the ITA test takers

    Automated assessment of second language comprehensibility: Review, training, validation, and generalization studies

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    Whereas many scholars have emphasized the relative importance of comprehensibility as an ecologically valid goal for L2 speech training, testing, and development, eliciting listenersโ€™ judgments is time-consuming. Following calls for research on more efficient L2 speech rating methods in applied linguistics, and growing attention toward using machine learning on spontaneous unscripted speech in speech engineering, the current study examined the possibility of establishing quick and reliable automated comprehensibility assessments. Orchestrating a set of phonological (maximum posterior probabilities and gaps between L1 and L2 speech), prosodic (pitch and intensity variation), and temporal measures (articulation rate, pause frequency), the regression model significantly predicted how naรฏve listeners intuitively judged low, mid, high, and nativelike comprehensibility among 100 L1 and L2 speakersโ€™ picture descriptions. The strength of the correlation (r = .823 for machine vs. human ratings) was comparable to naรฏve listenersโ€™ interrater agreement (r = .760 for humans vs. humans). The findings were successfully replicated when the model was applied to a new dataset of 45 L1 and L2 speakers (r = .827) and tested under a more freely constructed interview task condition (r = .809)

    CAPT๋ฅผ ์œ„ํ•œ ๋ฐœ์Œ ๋ณ€์ด ๋ถ„์„ ๋ฐ CycleGAN ๊ธฐ๋ฐ˜ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต,2020. 2. ์ •๋ฏผํ™”.Despite the growing popularity in learning Korean as a foreign language and the rapid development in language learning applications, the existing computer-assisted pronunciation training (CAPT) systems in Korean do not utilize linguistic characteristics of non-native Korean speech. Pronunciation variations in non-native speech are far more diverse than those observed in native speech, which may pose a difficulty in combining such knowledge in an automatic system. Moreover, most of the existing methods rely on feature extraction results from signal processing, prosodic analysis, and natural language processing techniques. Such methods entail limitations since they necessarily depend on finding the right features for the task and the extraction accuracies. This thesis presents a new approach for corrective feedback generation in a CAPT system, in which pronunciation variation patterns and linguistic correlates with accentedness are analyzed and combined with a deep neural network approach, so that feature engineering efforts are minimized while maintaining the linguistically important factors for the corrective feedback generation task. Investigations on non-native Korean speech characteristics in contrast with those of native speakers, and their correlation with accentedness judgement show that both segmental and prosodic variations are important factors in a Korean CAPT system. The present thesis argues that the feedback generation task can be interpreted as a style transfer problem, and proposes to evaluate the idea using generative adversarial network. A corrective feedback generation model is trained on 65,100 read utterances by 217 non-native speakers of 27 mother tongue backgrounds. The features are automatically learnt in an unsupervised way in an auxiliary classifier CycleGAN setting, in which the generator learns to map a foreign accented speech to native speech distributions. In order to inject linguistic knowledge into the network, an auxiliary classifier is trained so that the feedback also identifies the linguistic error types that were defined in the first half of the thesis. The proposed approach generates a corrected version the speech using the learners own voice, outperforming the conventional Pitch-Synchronous Overlap-and-Add method.์™ธ๊ตญ์–ด๋กœ์„œ์˜ ํ•œ๊ตญ์–ด ๊ต์œก์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๊ณ ์กฐ๋˜์–ด ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์ˆ˜๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์Œ์„ฑ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ์ ์šฉํ•œ ์ปดํ“จํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐœ์Œ ๊ต์œก(Computer-Assisted Pronunciation Training; CAPT) ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๋˜ํ•œ ์ ๊ทน์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ˜„์กดํ•˜๋Š” ํ•œ๊ตญ์–ด ๋งํ•˜๊ธฐ ๊ต์œก ์‹œ์Šคํ…œ์€ ์™ธ๊ตญ์ธ์˜ ํ•œ๊ตญ์–ด์— ๋Œ€ํ•œ ์–ธ์–ดํ•™์  ํŠน์ง•์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์ง€ ์•Š๊ณ  ์žˆ์œผ๋ฉฐ, ์ตœ์‹  ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ  ๋˜ํ•œ ์ ์šฉ๋˜์ง€ ์•Š๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๊ฐ€๋Šฅํ•œ ์›์ธ์œผ๋กœ์จ๋Š” ์™ธ๊ตญ์ธ ๋ฐœํ™” ํ•œ๊ตญ์–ด ํ˜„์ƒ์— ๋Œ€ํ•œ ๋ถ„์„์ด ์ถฉ๋ถ„ํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ์žˆ์–ด๋„ ์ด๋ฅผ ์ž๋™ํ™”๋œ ์‹œ์Šคํ…œ์— ๋ฐ˜์˜ํ•˜๊ธฐ์—๋Š” ๊ณ ๋„ํ™”๋œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด ์žˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ CAPT ๊ธฐ์ˆ  ์ „๋ฐ˜์ ์œผ๋กœ๋Š” ์‹ ํ˜ธ์ฒ˜๋ฆฌ, ์šด์œจ ๋ถ„์„, ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•๊ณผ ๊ฐ™์€ ํŠน์ง• ์ถ”์ถœ์— ์˜์กดํ•˜๊ณ  ์žˆ์–ด์„œ ์ ํ•ฉํ•œ ํŠน์ง•์„ ์ฐพ๊ณ  ์ด๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ถœํ•˜๋Š” ๋ฐ์— ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•œ ์‹ค์ •์ด๋‹ค. ์ด๋Š” ์ตœ์‹  ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ์ด ๊ณผ์ • ๋˜ํ•œ ๋ฐœ์ „์˜ ์—ฌ์ง€๊ฐ€ ๋งŽ๋‹ค๋Š” ๋ฐ”๋ฅผ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋จผ์ € CAPT ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์— ์žˆ์–ด ๋ฐœ์Œ ๋ณ€์ด ์–‘์ƒ๊ณผ ์–ธ์–ดํ•™์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์™ธ๊ตญ์ธ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ๊ณผ ํ•œ๊ตญ์–ด ์›์–ด๋ฏผ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ์„ ๋Œ€์กฐํ•˜๊ณ  ์ฃผ์š”ํ•œ ๋ณ€์ด๋ฅผ ํ™•์ธํ•œ ํ›„, ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์˜์‚ฌ์†Œํ†ต์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”๋„๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ข…์„ฑ ์‚ญ์ œ์™€ 3์ค‘ ๋Œ€๋ฆฝ์˜ ํ˜ผ๋™, ์ดˆ๋ถ„์ ˆ ๊ด€๋ จ ์˜ค๋ฅ˜๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ์— ์šฐ์„ ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ต์ •๋œ ํ”ผ๋“œ๋ฐฑ์„ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์€ CAPT ์‹œ์Šคํ…œ์˜ ์ค‘์š”ํ•œ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด ๊ณผ์ œ๊ฐ€ ๋ฐœํ™”์˜ ์Šคํƒ€์ผ ๋ณ€ํ™”์˜ ๋ฌธ์ œ๋กœ ํ•ด์„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณด์•˜์œผ๋ฉฐ, ์ƒ์„ฑ์  ์ ๋Œ€ ์‹ ๊ฒฝ๋ง (Cycle-consistent Generative Adversarial Network; CycleGAN) ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. GAN ๋„คํŠธ์›Œํฌ์˜ ์ƒ์„ฑ๋ชจ๋ธ์€ ๋น„์›์–ด๋ฏผ ๋ฐœํ™”์˜ ๋ถ„ํฌ์™€ ์›์–ด๋ฏผ ๋ฐœํ™” ๋ถ„ํฌ์˜ ๋งคํ•‘์„ ํ•™์Šตํ•˜๋ฉฐ, Cycle consistency ์†์‹คํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋ฐœํ™”๊ฐ„ ์ „๋ฐ˜์ ์ธ ๊ตฌ์กฐ๋ฅผ ์œ ์ง€ํ•จ๊ณผ ๋™์‹œ์— ๊ณผ๋„ํ•œ ๊ต์ •์„ ๋ฐฉ์ง€ํ•˜์˜€๋‹ค. ๋ณ„๋„์˜ ํŠน์ง• ์ถ”์ถœ ๊ณผ์ •์ด ์—†์ด ํ•„์š”ํ•œ ํŠน์ง•๋“ค์ด CycleGAN ํ”„๋ ˆ์ž„์›Œํฌ์—์„œ ๋ฌด๊ฐ๋… ๋ฐฉ๋ฒ•์œผ๋กœ ์Šค์Šค๋กœ ํ•™์Šต๋˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ์–ธ์–ด ํ™•์žฅ์ด ์šฉ์ดํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ์–ธ์–ดํ•™์  ๋ถ„์„์—์„œ ๋“œ๋Ÿฌ๋‚œ ์ฃผ์š”ํ•œ ๋ณ€์ด๋“ค ๊ฐ„์˜ ์šฐ์„ ์ˆœ์œ„๋Š” Auxiliary Classifier CycleGAN ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ CycleGAN์— ์ง€์‹์„ ์ ‘๋ชฉ์‹œ์ผœ ํ”ผ๋“œ๋ฐฑ ์Œ์„ฑ์„ ์ƒ์„ฑํ•จ๊ณผ ๋™์‹œ์— ํ•ด๋‹น ํ”ผ๋“œ๋ฐฑ์ด ์–ด๋–ค ์œ ํ˜•์˜ ์˜ค๋ฅ˜์ธ์ง€ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Š” ๋„๋ฉ”์ธ ์ง€์‹์ด ๊ต์ • ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ ๋‹จ๊ณ„๊นŒ์ง€ ์œ ์ง€๋˜๊ณ  ํ†ต์ œ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค๋Š” ๋ฐ์— ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ 27๊ฐœ์˜ ๋ชจ๊ตญ์–ด๋ฅผ ๊ฐ–๋Š” 217๋ช…์˜ ์œ ์˜๋ฏธ ์–ดํœ˜ ๋ฐœํ™” 65,100๊ฐœ๋กœ ํ”ผ๋“œ๋ฐฑ ์ž๋™ ์ƒ์„ฑ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๊ณ , ๊ฐœ์„  ์—ฌ๋ถ€ ๋ฐ ์ •๋„์— ๋Œ€ํ•œ ์ง€๊ฐ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€์„ ๋•Œ ํ•™์Šต์ž ๋ณธ์ธ์˜ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ์œ ์ง€ํ•œ ์ฑ„ ๊ต์ •๋œ ๋ฐœ์Œ์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ•์ธ ์Œ๋†’์ด ๋™๊ธฐ์‹ ์ค‘์ฒฉ๊ฐ€์‚ฐ (Pitch-Synchronous Overlap-and-Add) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ์ƒ๋Œ€ ๊ฐœ์„ ๋ฅ  16.67%์ด ํ™•์ธ๋˜์—ˆ๋‹ค.Chapter 1. Introduction 1 1.1. Motivation 1 1.1.1. An Overview of CAPT Systems 3 1.1.2. Survey of existing Korean CAPT Systems 5 1.2. Problem Statement 7 1.3. Thesis Structure 7 Chapter 2. Pronunciation Analysis of Korean Produced by Chinese 9 2.1. Comparison between Korean and Chinese 11 2.1.1. Phonetic and Syllable Structure Comparisons 11 2.1.2. Phonological Comparisons 14 2.2. Related Works 16 2.3. Proposed Analysis Method 19 2.3.1. Corpus 19 2.3.2. Transcribers and Agreement Rates 22 2.4. Salient Pronunciation Variations 22 2.4.1. Segmental Variation Patterns 22 2.4.1.1. Discussions 25 2.4.2. Phonological Variation Patterns 26 2.4.1.2. Discussions 27 2.5. Summary 29 Chapter 3. Correlation Analysis of Pronunciation Variations and Human Evaluation 30 3.1. Related Works 31 3.1.1. Criteria used in L2 Speech 31 3.1.2. Criteria used in L2 Korean Speech 32 3.2. Proposed Human Evaluation Method 36 3.2.1. Reading Prompt Design 36 3.2.2. Evaluation Criteria Design 37 3.2.3. Raters and Agreement Rates 40 3.3. Linguistic Factors Affecting L2 Korean Accentedness 41 3.3.1. Pearsons Correlation Analysis 41 3.3.2. Discussions 42 3.3.3. Implications for Automatic Feedback Generation 44 3.4. Summary 45 Chapter 4. Corrective Feedback Generation for CAPT 46 4.1. Related Works 46 4.1.1. Prosody Transplantation 47 4.1.2. Recent Speech Conversion Methods 49 4.1.3. Evaluation of Corrective Feedback 50 4.2. Proposed Method: Corrective Feedback as a Style Transfer 51 4.2.1. Speech Analysis at Spectral Domain 53 4.2.2. Self-imitative Learning 55 4.2.3. An Analogy: CAPT System and GAN Architecture 57 4.3. Generative Adversarial Networks 59 4.3.1. Conditional GAN 61 4.3.2. CycleGAN 62 4.4. Experiment 63 4.4.1. Corpus 64 4.4.2. Baseline Implementation 65 4.4.3. Adversarial Training Implementation 65 4.4.4. Spectrogram-to-Spectrogram Training 66 4.5. Results and Evaluation 69 4.5.1. Spectrogram Generation Results 69 4.5.2. Perceptual Evaluation 70 4.5.3. Discussions 72 4.6. Summary 74 Chapter 5. Integration of Linguistic Knowledge in an Auxiliary Classifier CycleGAN for Feedback Generation 75 5.1. Linguistic Class Selection 75 5.2. Auxiliary Classifier CycleGAN Design 77 5.3. Experiment and Results 80 5.3.1. Corpus 80 5.3.2. Feature Annotations 81 5.3.3. Experiment Setup 81 5.3.4. Results 82 5.4. Summary 84 Chapter 6. Conclusion 86 6.1. Thesis Results 86 6.2. Thesis Contributions 88 6.3. Recommendations for Future Work 89 Bibliography 91 Appendix 107 Abstract in Korean 117 Acknowledgments 120Docto

    Listener Background in L2 Speech Evaluation

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    Listeners are integral parts of second language (L2) oral performance assessment. However, evaluation of listeners is susceptible to listener background variables and biases. These variables and preexisting biases distort native speaker (NS) listenersโ€™ perceptions of non-native speakersโ€™ (NNSs) speech performance and contribute errors into their oral performance assessment. Among listener background variables, listenersโ€™ first language status, the amount of exposure to different English varieties, listenersโ€™ educational background, prior language teaching experience, NNSsโ€™ linguistic stereotyping, and listener attitude have been investigated in the literature and assumed to exert sizable amount of variation in speakersโ€™ oral proficiency true scores. To minimize listenersโ€™ bias in the assessment context, listeners are provided with intensive training programs in which they are trained how to rate NNSsโ€™ speech more objectively utilizing scoring rubrics. To mediate listenersโ€™ bias in social contexts, the literature has provided strands of evidence in favor of structured intergroup contact programs, which are inoculations particularly devised to improve NSsโ€™ attitude, thereby making them more receptive to NNSsโ€™ English varieties. To enhance L2 listenersโ€™ self-efficacy and foster their autonomy, L2 instructors are encouraged to emphasize explicit instruction of listening strategies
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