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    Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

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    Self-imitating feedback is an effective and learner-friendly method for non-native learners in Computer-Assisted Pronunciation Training. Acoustic characteristics in native utterances are extracted and transplanted onto learner's own speech input, and given back to the learner as a corrective feedback. Previous works focused on speech conversion using prosodic transplantation techniques based on PSOLA algorithm. Motivated by the visual differences found in spectrograms of native and non-native speeches, we investigated applying GAN to generate self-imitating feedback by utilizing generator's ability through adversarial training. Because this mapping is highly under-constrained, we also adopt cycle consistency loss to encourage the output to preserve the global structure, which is shared by native and non-native utterances. Trained on 97,200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback. Furthermore, the transformed spectrogram shows segmental corrections that cannot be obtained by prosodic transplantation. Perceptual test comparing the self-imitating and correcting abilities of our method with the baseline PSOLA method shows that the generative approach with cycle consistency loss is promising

    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

    More is more in language learning:reconsidering the less-is-more hypothesis

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    The Less-is-More hypothesis was proposed to explain age-of-acquisition effects in first language (L1) acquisition and second language (L2) attainment. We scrutinize different renditions of the hypothesis by examining how learning outcomes are affected by (1) limited cognitive capacity, (2) reduced interference resulting from less prior knowledge, and (3) simplified language input. While there is little-to-no evidence of benefits of limited cognitive capacity, there is ample support for a More-is-More account linking enhanced capacity with better L1- and L2-learning outcomes, and reduced capacity with childhood language disorders. Instead, reduced prior knowledge (relative to adults) may afford children with greater flexibility in inductive inference; this contradicts the idea that children benefit from a more constrained hypothesis space. Finally, studies of childdirected speech (CDS) confirm benefits from less complex input at early stages, but also emphasize how greater lexical and syntactic complexity of the input confers benefits in L1-attainment

    Nonword Repetition and Interactions Among Vocabulary, Phonotactic probability, and Phonological Awareness in Four Linguistic Groups

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    The current study was designed to compare the English nonword repetition accuracy in 7-year-old monolingual English, Koreanโ€“English bilingual, Chineseโ€“English bilingual, and Spanishโ€“English bilingual children. The relationships among nonword repetition accuracy, vocabulary, phonological awareness, and phonotactic probability in each group of children were also examined. The results indicated significant differences among the groupsโ€™ accuracy of consonants and vowels by syllable length. Different correlational patterns emerged among nonword repetition accuracy, vocabulary, and phonological awareness. Theoretical and clinical implications for the use of nonword repetition tasks for children from various linguistic backgrounds are discussed

    Focusing on Rater Variability and Phonetic Correlates

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์™ธ๊ตญ์–ด๊ต์œก๊ณผ(์˜์–ด์ „๊ณต), 2022. 8. ์•ˆํ˜„๊ธฐ.์œ ์ฐฝ์„ฑ์€ ์ œ2์–ธ์–ด ์ˆ˜ํ–‰๊ณผ ์–ธ์–ด๋Šฅ๋ ฅ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ๋ถ€๋ถ„์„ ๊ตฌ์„ฑํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋งŽ์€ ์ œ2์–ธ์–ด ํ•™์Šต์ž๊ฐ€ ์œ ์ฐฝ์„ฑ์„ ํš๋“ํ•˜๊ณ ์ž ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ œ2์–ธ์–ด ์—ฐ๊ตฌ์ž๋‚˜ EFL(์™ธ๊ตญ์–ด๋กœ์„œ์˜ ์˜์–ด) ๊ต์œก์ž๋“ค์€ ์œ ์ฐฝ์„ฑ์— ๋Œ€ํ•ด ๋ช…ํ™•ํžˆ ์ดํ•ดํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ ๊ฐœ๋… ๋˜ํ•œ ์ผ๊ด€์„ฑ ์žˆ๊ฒŒ ์ •์˜ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ EFL ํ™˜๊ฒฝ์—์„œ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€์ž๋“ค์ด ์–ด๋–ป๊ฒŒ ์œ ์ฐฝ์„ฑ์„ ์ธ์‹ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งค์šฐ ๋ถ€์กฑํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ•™๋ฌธ์ ์ธ ํ•„์š”์„ฑ์„ ์ถฉ์กฑํ•˜๊ณ , ์œ ์ฐฝ์„ฑ์˜ ๋‹ค์ฐจ์›์  ๊ตฌ์„ฑ์— ๋Œ€ํ•œ ๊นŠ์€ ์ดํ•ด๋ฅผ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ์›์–ด๋ฏผ ๊ต์‚ฌ, ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ, ๊ทธ๋ฆฌ๊ณ  ๋™๋ฃŒ ํ•™์ƒ๋“ค์ด ํ•œ๊ตญ ๊ณ ๋“ฑํ•™์ƒ์˜ ์˜์–ด ๋งํ•˜๊ธฐ ์œ ์ฐฝ์„ฑ์„ ์–ด๋–ป๊ฒŒ ์ธ์‹ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š”์ง€๋ฅผ ์ธ์‹ ์œ ์ฐฝ์„ฑ๊ณผ ๋ฐœํ™” ์œ ์ฐฝ์„ฑ์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๊ด€์ ์—์„œ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ 1์€ ํ˜ผํ•ฉ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์„ธ ํ‰๊ฐ€ ์ง‘๋‹จ์˜ ์ธ์‹ ์œ ์ฐฝ์„ฑ์ด ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅธ์ง€๋ฅผ ๋น„๊ตํ•œ ๊ฒƒ์ด๋‹ค. ์„ธ ์ง‘๋‹จ์˜ ํ‰๊ฐ€์ž๋Š” ๋‹ค๋ฅธ ๋งํ•˜๊ธฐ ๋Šฅ๋ ฅ(์ƒ, ์ค‘, ํ•˜)์„ ๊ฐ€์ง„ ๋ฐœํ™”์ž๊ฐ€ ๋‘ ๊ณผ์ œ ์œ ํ˜•(๊ทธ๋ฆผ ์ด์•ผ๊ธฐ, ์ž์œ  ๋ฐœํ™”)์— ๋Œ€ํ•ด ์ˆ˜ํ–‰ํ•œ ์ƒ˜ํ”Œ์„ ๋“ฃ๊ณ  ์œ ์ฐฝ์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๊ณ , ์—ฐ๊ตฌ์ž๋Š” ์„ธ ์ง‘๋‹จ์˜ ์ „๋ฐ˜์ ์ธ ์œ ์ฐฝ์„ฑ ์ ์ˆ˜๋Š” ์–‘์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ, ์ฑ„์ ์ž์˜ ์„œ๋ฉด ํ‰๊ฐ€๋Š” ์งˆ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋‘ ๊ณผ์ œ์™€ ์„ธ ์–ธ์–ด ๋Šฅ๋ ฅ ์ง‘๋‹จ ๋ชจ๋‘์—๊ฒŒ์„œ ์›์–ด๋ฏผ๊ณผ ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ ์ง‘๋‹จ์€ ๋น„์Šทํ•œ ์ˆ˜์ค€์˜ ์—„๊ฒฉ์„ฑ ํŒจํ„ด์„ ๋ณด์˜€์ง€๋งŒ ๋™๋ฃŒ ํ•™์ƒ ์ง‘๋‹จ์€ ๊ต์‚ฌ ์ง‘๋‹จ์— ๋น„ํ•ด ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚ฎ์€ ์ ์ˆ˜๋ฅผ ์ฃผ์—ˆ์Œ์ด ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ์ด์–ด์ง„ ์งˆ์  ๋ถ„์„์€ EFL ๊ต์‚ฌ ์ง‘๋‹จ๊ณผ ๋™๋ฃŒ ํ•™์ƒ ์ง‘๋‹จ์˜ ์ฐจ์ด๋ฅผ ๋‹ค์‹œ ํ•œ๋ฒˆ ํ™•์ธ์‹œ์ผœ์ฃผ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์„ธ ํ‰๊ฐ€ ์ง‘๋‹จ์ด ํ•˜ ์ˆ˜์ค€์˜ ํ•™์ƒ๋“ค์„ ํ‰๊ฐ€ํ•  ๋•Œ, ๊ทธ๋ฆผ ์ด์•ผ๊ธฐ์—๋Š” ๋‚ฎ์€ ์ ์ˆ˜๋ฅผ ์ค€ ๋ฐ˜๋ฉด ์ž์œ  ๋ฐœํ™”์—๋Š” ๋†’์€ ์ ์ˆ˜๋ฅผ ์ฃผ๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์—ฌ, ๋ชจ๋“  ํ‰๊ฐ€ ์ง‘๋‹จ์ด ๊ณผ์ œ ์œ ํ˜•์— ์ƒ๋‹นํ•œ ์˜ํ–ฅ์„ ๋ฐ›์Œ์ด ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ์—ฐ๊ตฌ 1์—์„œ ๋ฐœ๊ฒฌ๋œ ์„ธ ๊ทธ๋ฃน์˜ ์œ ์ฐฝ์„ฑ ์ธ์‹์˜ ์ฐจ์ด๋Š” ์—ฐ๊ตฌ 2์—์„œ ์ข€ ๋” ๋’ท๋ฐ›์นจ๋œ๋‹ค. ์—ฐ๊ตฌ 2์—์„œ๋Š” ๋ฐœํ™” ์œ ์ฐฝ์„ฑ์˜ ํŠน์„ฑ ์ค‘ ์–ด๋–ค ์š”์†Œ๋‚˜ ์–ด๋–ค ์Œํ–ฅ ๋ชจ๋ธ์ด ํ‰๊ฐ€์ž๋“ค์˜ ์ธ์‹ ์œ ์ฐฝ์„ฑ์„ ๊ฐ€์žฅ ์ž˜ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š”์ง€, ๋˜ํ•œ ์–ด๋–ค ๋ฐœํ™” ์œ ์ฐฝ์„ฑ ํŠน์„ฑ์ด ํ‰๊ฐ€์ž๋กœ ํ•˜์—ฌ๊ธˆ ๋ฐœํ™”์ž์˜ ์œ ์ฐฝ์„ฑ ์ˆ˜์ค€์„ ํŒ๋‹จํ•˜๋Š” ๋ฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋ฐœํ™” ์œ ์ฐฝ์„ฑ๊ณผ ์ธ์‹ ์œ ์ฐฝ์„ฑ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๋‘ ๊ฐ€์ง€ ์†๋„์™€ ๊ด€๋ จ๋œ ํŠน์งˆ(ํ‰๊ท  ๋ฐœํ™” ๊ธธ์ด, ๋ฐœํ™” ์†๋„)๊ณผ ํœด์ง€์™€ ๊ด€๋ จ๋œ ํŠน์งˆ(์ ˆ ๋‚ด ๋ฌด์Œ ํœด์ง€๊ธฐ ๋น„์œจ, ํ‰๊ท  ๋ฌด์Œ ํœด์ง€ ๊ธธ์ด)์ด ์„ธ ํ‰๊ฐ€ ์ง‘๋‹จ์˜ ์ธ์‹ ์œ ์ฐฝ์„ฑ๊ณผ ๊ฐ€์žฅ ํฐ ์ƒ๊ด€์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํšŒ๊ท€ ๋ถ„์„ ๊ฒฐ๊ณผ ํ‰๊ท  ๋ฐœํ™” ๊ธธ์ด์™€ ํ‰๊ท  ๋ฌด์Œ ํœด์ง€ ๊ธธ์ด๊ฐ€ ํšŒ๊ท€ ๋ชจํ˜•์˜ ๊ฐ€์žฅ ๋งŽ์€ ๋ณ€ํ™”๋Ÿ‰์„ ์„ค๋ช…ํ•˜๋ฉฐ ์„ธ ํ‰๊ฐ€ ์ง‘๋‹จ์˜ ์œ ์ฐฝ์„ฑ ์ ์ˆ˜๋ฅผ ๊ฐ€์žฅ ์ž˜ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ํ•˜์ง€๋งŒ ์›์–ด๋ฏผ๊ณผ ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ ์ง‘๋‹จ์˜ ๋ชจ๋ธ์€ ์ž…๋ ฅ ๋ณ€์ˆ˜์™€ ๋ณ€์ˆ˜์˜ ์ƒ๋Œ€์ ์ธ ์ˆœ์œ„ ์ธก๋ฉด์—์„œ ์™„์ „ํžˆ ๋™์ผํ•œ ๋ฐ˜๋ฉด ๋™๋ฃŒ ํ•™์ƒ ์ง‘๋‹จ์˜ ํšŒ๊ท€ ๋ชจํ˜•์€ ๊ต์‚ฌ ์ง‘๋‹จ์˜ ๋ชจํ˜•๊ณผ ์ฐจ์ด๊ฐ€ ์žˆ์Œ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌด์Œ ํœด์ง€์˜ ํ‰๊ท  ๊ธธ์ด์™€ ๊ฐ™์€ ํœด์ง€์™€ ๊ด€๋ จ๋œ ํŠน์งˆ์€ ํ•˜ ์ˆ˜์ค€์˜ ์œ ์ฐฝ์„ฑ์„ ๊ฐ€์ง„ ํ•™์ƒ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๊ณ , ๋ฐœํ™” ์†๋„์™€ ๊ฐ™์€ ์†๋„ ๊ด€๋ จ ํŠน์งˆ์€ ์ƒ ์ˆ˜์ค€์˜ ์œ ์ฐฝ์„ฑ์„ ๊ฐ€์ง„ ํ•™์ƒ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์Œ์ด ๋ฐํ˜€์กŒ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์€ ์›์–ด๋ฏผ ๊ต์‚ฌ์™€ ๋น„๊ตํ•˜์—ฌ ์œ ์ฐฝ์„ฑ ํ‰๊ฐ€์ž๋กœ์„œ ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ๋“ค์˜ ์—ญํ• ์— ๋Œ€ํ•œ ๋…ผ์˜์— ๋ฐ”ํƒ•์ด ๋˜์—ˆ๊ณ , ๋™๋ฃŒ ํ•™์ƒ ํ‰๊ฐ€์˜ ํƒ€๋‹น์„ฑ๊ณผ ์‹ ๋ขฐ์„ฑ์„ ๋…ผ์˜ํ•˜๋Š” ํ† ๋Œ€๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. ์—ฐ๊ตฌ 1๊ณผ ์—ฐ๊ตฌ 2์—์„œ ๋“œ๋Ÿฌ๋‚œ ์—ฌ๋Ÿฌ ์‹ค์ฆ์ ์ธ ์ฆ๊ฑฐ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ์›์–ด๋ฏผ๊ณผ ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ๋Š” ๋ชจ๋‘ ์ œ2์–ธ์–ด๋กœ์„œ ์˜์–ด ๋งํ•˜๊ธฐ ์œ ์ฐฝ์„ฑ์„ ๋น„์Šทํ•œ ๋ฐฉ์‹์œผ๋กœ ์ธ์‹ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ฒฐ๋ก  ์ง€์„ ์ˆ˜ ์žˆ๊ณ , ๋น„์›์–ด๋ฏผ ๊ต์‚ฌ ํ‰๊ฐ€์ž๋„ ์›์–ด๋ฏผ ๊ต์‚ฌ ํ‰๊ฐ€์ž์™€ ๊ฐ™์ด ์œ ์ฐฝ์„ฑ ํ‰๊ฐ€์— ๋™์ผํ•œ ํ‰๊ฐ€์ž๋กœ ๊ธฐ๋Šฅํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋™๋ฃŒ ํ•™์ƒ ์ง‘๋‹จ์€ ๊ต์‚ฌ ์ง‘๋‹จ์— ๋น„ํ•ด ๋‹ค๋ฅธ ํ‰๊ฐ€ ์–‘์ƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๊ณ , ์ด๋Š” ์ด๋“ค์ด ํ•œ๊ตญ์˜ EFL ํ™˜๊ฒฝ์—์„œ ๋Šฅ์ˆ™ํ•œ ์œ ์ฐฝ์„ฑ ํ‰๊ฐ€์ž๋กœ์„œ์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋งŽ์€ ๊ต์œก์ ์ธ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ํ˜„ ๋…ผ๋ฌธ์€ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€ ์ง‘๋‹จ์ด ํ•™์ƒ๋“ค์˜ ์˜์–ด ๋งํ•˜๊ธฐ ์œ ์ฐฝ์„ฑ์„ ์–ด๋–ป๊ฒŒ ์ธ์‹ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š”์ง€๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ๋ถ„์„ํ•˜๋ฉฐ, ํ•œ๊ตญ EFL ํ™˜๊ฒฝ์—์„œ ํƒ€๋‹นํ•˜๊ณ  ์‹ ๋ขฐ์„ฑ์ด ์žˆ๋Š” ์œ ์ฐฝ์„ฑ ํ‰๊ฐ€ ๋ฐฉ์‹์„ ์ˆ˜๋ฆฝํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•œ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋™๋ฃŒ ํ•™์ƒ๋“ค์˜ ํ‰๊ฐ€๋ฅผ ๊ต์‚ฌ ์ง‘๋‹จ์˜ ํ‰๊ฐ€์™€ ๋น„๊ตํ•˜์—ฌ ์—ฐ๊ตฌํ•จ์œผ๋กœ์„œ ๋™๋ฃŒ ํ‰๊ฐ€์˜ ๊ฐ€๋Šฅ์„ฑ๊ณผ ํ•œ๊ณ„์— ๋Œ€ํ•œ ์ง์ ‘์ ์ธ ์ฆ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ๊ณผ ๊ด€๋ จํ•˜์—ฌ ๋ณธ ์—ฐ๊ตฌ๋Š” ์œ ์ฐฝ์„ฑ์˜ ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์ธ ์ธ์‹ ์œ ์ฐฝ์„ฑ๊ณผ ๋ฐœํ™” ์œ ์ฐฝ์„ฑ์„ ๊ฒฐํ•ฉํ•จ์œผ๋กœ์„œ ์œ ์ฐฝ์„ฑ์˜ ๋‹ค์ฐจ์›์  ๊ตฌ์„ฑ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๋„์›€์„ ์ฃผ๊ณ , ์–‘์ ์ธ ๋ฐฉ๋ฒ•๋ก ๊ณผ ์งˆ์ ์ธ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒฐํ•ฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€ ์ง‘๋‹จ์˜ ํ‰๊ฐ€ ํŒจํ„ด์„ ์ข…ํ•ฉ์ ์œผ๋กœ ์ดํ•ดํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•œ๋‹ค.Fluency constitutes a crucial aspect of understanding second language (L2) performance and proficiency, and attaining high levels of fluency is one essential goal for many L2 learners. However, fluency has not been well understood, and the term has not been used consistently by L2 researchers and EFL educators. In addition, there is a paucity of studies concerning how raters in the EFL context perceive and evaluate fluency. To fill the academic gap and deepen understanding of the multidimensional construct of fluency, the current dissertation investigated how Korean English teachers, native English teachers, and peer students perceive and rate Korean high school students' speaking fluency in terms of perceived fluency and utterance fluency. Study 1 investigated the differences in perceived fluency by three rater groups, employing a mix-method approach. Overall fluency ratings across two task types (picture narration, spontaneous speech) at speakers' different oral proficiency levels (low, mid, high) were analyzed quantitatively, and raters' written comments were examined qualitatively. The native and non-native teacher groups showed comparable severity patterns, but the peer group provided significantly lower fluency rating scores than the two EFL teacher groups on both tasks across all proficiency levels. The following qualitative analyses confirmed the discrepancy between the two EFL teacher groups and the peer group. In addition, it was revealed that the three rater groups' evaluations for low-level learners were significantly affected by task types, with the spontaneous speech task scoring higher than the picture narration task. The disparities in the three groups' perceptions of fluency reported in Study 1 were further supported and accounted for in Study 2. Study 2 examined the relationship between utterance fluency and perceived fluency to determine which acoustic model best predicted the three listener groups' perceived fluency and which acoustic features were associated with the three groups' decision-making of speakers' fluency levels. Two speed features (i.e., mean length of run, articulation rate) and two breakdown measures (i.e., silent pause rate within a clause, mean length of silent pauses) were found to be most strongly correlated with their perceived fluency. The regression analysis indicated that the mean length of run and the mean length of silent pauses were the two strongest predictors for the three rater groups, explaining most of the variance in the three regression models. However, the data further revealed that the regression models for native and non-native teachers were identical regarding the four entered variables and their relative contribution rankings, while the best regression model for the peer group showed some disparities. In addition, it was found that breakdown measures, such as the mean length of silent pauses, helped to distinguish the low-level from higher level (mid, high) groups, while speed measures, such as articulation rate, discriminated the high-level group from lower level (low, mid) groups. These findings served as a foundation for a discussion of native versus non-native English teachers as fluency assessors on the one hand and the validity and reliability of peer assessment on the other. Based on empirical evidence drawn from Study 1 and 2, it can be concluded that native and non-native English teachers perceived and rated L2 fluency in a similar way, confirming that non-native teachers are as equally capable of serving as fluency raters as native teachers are. However, the peer group displayed rating patterns distinct from those of the teacher group, implying that much pedagogical effortย is required to prepare peer students to serve as competent fluency raters in the Korean EFL context. The current dissertation contributes to establishing a valid and reliable fluency assessment in the Korean EFL context by systematically analyzing how various groups of raters perceive and evaluate students' English speaking fluency. In addition, the study provides direct evidence regarding the possibility and limitations of peer assessment by comparing the peer group's judgments with those of the teacher groups. Regarding research methodology, the study contributes to illuminating the multidimensional constructs of fluency by combining two facets of fluency, like perceived fluency and utterance fluency. It is also shown that a comprehensive understanding of rating patterns drawn by different raters can be achieved by combining quantitative and qualitative research methods.CHAPTER 1. INTRODUCTION 1 1.1 Aims of Study 1 1.2 Background of Study 2 1.3 Research Questions 7 1.4 Organization of the Dissertation 9 CHAPTER 2. LITERATURE REVIEW 11 2.1 Defining and Measuring Fluency 11 2.2 Perceived Fluency 18 2.2.1 Rater Variables on Fluency Ratings 18 2.2.2 Task Variables on Fluency Ratings 26 2.3 Utterance Fluency 30 2.3.1 Predictors of Utterance Fluency 30 2.3.1.1 Speed Fluency 31 2.3.1.2 Breakdown Fluency 34 2.3.1.3 Repair Fluency 39 2.3.2 Utterance Fluency Model 42 2.3.3 Utterance Fluency Features and Fluency levels 46 CHAPTER 3. Study 1: PERCEIVED FLUENCY 49 3.1 Methodology 49 3.1.1 Participants 50 3.1.2 Instruments 53 3.1.3 Procedures 55 3.1.4 Data Analysis 57 3.2 Results 59 3.2.1 A Quantitative Study 59 3.2.1.1 Comparison of the Three Rater Groups 59 3.2.1.2 Effects of Raters and Task Types on Fluency Ratings 64 3.2.2 A Qualitative Study 73 3.3 Summary and Discussion 87 CHAPTER 4. Study 2: UTTERANCE FLUENCY 96 4.1 Methodology 96 4.1.1 Participants and Procedures 97 4.1.2 Temporal Measures 98 4.1.3 Acoustic Analysis 101 4.1.4 Statistical Analysis 103 4.2 Results 106 4.2.1 Predictors of Three Rater Groupsโ€™ Fluency Ratings 106 4.2.2 A Best Prediction Model on L2 Speaking Fluency 113 4.2.3 Utterance Measures Distinguishing Fluency Levels 119 4.3 Summary and Discussion 125 CHAPTER 5. CONCLUSION 134 5.1 Findings and Pedagogical Implications 134 5.2 Limitations and Suggestions for Future Research 143 REFERENCES 145 APPENDICES 155 ABSTRACT IN KOREAN 163๋ฐ•

    THE RELATIONSHIP BETWEEN ACOUSTIC FEATURES OF SECOND LANGUAGE SPEECH AND LISTENER EVALUATION OF SPEECH QUALITY

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    Second language (L2) speech is typically less fluent than native speech, and differs from it phonetically. While the speech of some L2 English speakers seems to be easily understood by native listeners despite the presence of a foreign accent, other L2 speech seems to be more demanding, such that listeners must expend considerable effort in order to understand it. One reason for this increased difficulty may simply be the speakerโ€™s pronunciation accuracy or phonetic intelligibility. If a L2 speakerโ€™s pronunciations of English sounds differ sufficiently from the sounds that native listeners expect, these differences may force native listeners to work much harder to understand the divergent speech patterns. However, L2 speakers also tend to differ from native ones in terms of fluency โ€“ the degree to which a speaker is able to produce appropriately structured phrases without unnecessary pauses, self-corrections or restarts. Previous studies have shown that measures of fluency are strongly predictive of listenersโ€™ subjective ratings of the acceptability of L2 speech: Less fluent speech is consistently considered less acceptable (Ginther, Dimova, & Yang, 2010). However, since less fluent speakers tend also to have less accurate pronunciations, it is unclear whether or how these factors might interact to influence the amount of effort listeners exert to understand L2 speech, nor is it clear how listening effort might relate to perceived quality or acceptability of speech. In this dissertation, two experiments were designed to investigate these questions

    Comparing the outcomes of early and late acquisition of european portuguese: an analysis of morpho-syntactic and phonetic performance

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    The present paper compares the linguistic competence of German-Portuguese bilinguals with upper-intermediate German L2 learners (L2ers) of EP (European Portuguese) and with monolingual Portuguese speakers. The bilingual speakers are heritage speakers (HSs), who were raised bilingually with EP as the minority language and German as the majority language. The aim of our comparison is to verify in which way different input sources and maturational effects shape the speakersโ€™ linguistic knowledge. The findings of two studies, one focused on the morpho-syntactic knowledge of clitics and the other on global accent, corroborate the assumption that L2ers and HSs behave differently, despite superficial similarities observed in the morpho-syntactic study. In contrast to that of the L2ersโ€™, the accent of the HSs is perceived as being native-like, whereas their morpho-syntactic competence is mainly shaped by their dominant exposure to colloquial Portuguese and reduced contact with formal registers.info:eu-repo/semantics/publishedVersio

    The phonetics of second language learning and bilingualism

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    This chapter provides an overview of major theories and findings in the field of second language (L2) phonetics and phonology. Four main conceptual frameworks are discussed and compared: the Perceptual Assimilation Model-L2, the Native Language Magnet Theory, the Automatic Selection Perception Model, and the Speech Learning Model. These frameworks differ in terms of their empirical focus, including the type of learner (e.g., beginner vs. advanced) and target modality (e.g., perception vs. production), and in terms of their theoretical assumptions, such as the basic unit or window of analysis that is relevant (e.g., articulatory gestures, position-specific allophones). Despite the divergences among these theories, three recurring themes emerge from the literature reviewed. First, the learning of a target L2 structure (segment, prosodic pattern, etc.) is influenced by phonetic and/or phonological similarity to structures in the native language (L1). In particular, L1-L2 similarity exists at multiple levels and does not necessarily benefit L2 outcomes. Second, the role played by certain factors, such as acoustic phonetic similarity between close L1 and L2 sounds, changes over the course of learning, such that advanced learners may differ from novice learners with respect to the effect of a specific variable on observed L2 behavior. Third, the connection between L2 perception and production (insofar as the two are hypothesized to be linked) differs significantly from the perception-production links observed in L1 acquisition. In service of elucidating the predictive differences among these theories, this contribution discusses studies that have investigated L2 perception and/or production primarily at a segmental level. In addition to summarizing the areas in which there is broad consensus, the chapter points out a number of questions which remain a source of debate in the field today.https://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHAccepted manuscriptAccepted manuscrip

    Factors Influencing Intelligibility and Comprehensibility: A Critical Review of Research on Second Language English Speakers

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    The varieties of English have led to the paradigm of Global Englishes, which describes the ideologies of English as a lingua franca (ELF) and World Englishes (differential uses of English internationally) in diverse sociolinguistic contexts. Global Englishes literature complicates intelligibility and comprehensibility since it tends to problematize โ€œnative normsโ€ as the only benchmarks for successful lingua franca use. Intelligibility and comprehensibility studies have recently been concerned with the interaction between non-native speakers using English as a second language for communication. Thus, this paper critically evaluates research on intelligibility and comprehensibility of second language (L2) English speakers. It is observed that various speaker, listener, and contextual factors may affect intelligibility and comprehensibility. Based on the influencing factors, this paper also makes several recommendations for how intelligibility and comprehensibility can be improved. It is suggested that further research is needed for L2 instruction that may be promising for improving intelligibility and comprehensibility
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