Статті в журналах: "Spoken English"

1

Ramesh, S., and M. Suresh Kumar. "Teaching Spoken English." Journal of Advances and Scholarly Researches in Allied Education 15, no. 9 (October 1, 2018): 34–36. http://dx.doi.org/10.29070/15/57913.

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2

Heptonstall, G. "Spoken/Broken." English 46, no. 184 (March 1, 1997): 60–61. http://dx.doi.org/10.1093/english/46.184.60.

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3

Rodriguez, Fatima, Sandra E. Echeverria, Sri Ram Pentakota, Chioma Amadi, Katherine G. Hastings, and Latha P. Palaniappan. "Comparison of Ideal Cardiovascular Health Attainment and Acculturation among Asian Americans and Latinos." Ethnicity & Disease 29, no. 2 (April 18, 2019): 287–96. http://dx.doi.org/10.18865/ed.29.2.287.

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Objective: To determine the association between language and ideal cardiovascular health among Asian Americans and Latinos.Design/ Study Participants: Cross-sectional study using 2011-2016 National Health and Nutrition Examination Survey of Asian Americans (n=2,009) and Latinos (n=3,906).Interventions: Participants were classified according to language spoken at home (only/mostly English spoken, both English and native language spoken equally, or mostly/only native language spoken).Outcomes: Ideal, intermediate and poor cardiovascular health status for smoking, blood pressure, glucose level, and total cholesterol.Results: The majority of Asian Americans and Latinos had ideal smoking status, but those who only/mostly spoke English were more likely to smoke compared with those who spoke only/mostly spoke their native language. Approximately one third of Asian Americans and Latinos had intermediate (ie, borderline or treated to goal) levels of cardiovascular health for blood pressure, glucose level and total cholesterol. In adjusted models, those who spoke only/mostly their native language were significantly less likely to have poor smoking or hypertension status than those who spoke only/ mostly English. Among Latinos, only/mostly Spanish speakers were more likely to have poor/ intermediate glucose levels (PR=1.35, 95% CI =1.21, 1.49) than those who spoke only/ mostly English, becoming statistically non-significant after adjusting for education and income.Conclusion: We found significant variation in ideal cardiovascular health attainment by language spoken at home in two of the largest immigrant groups in the United States. Findings suggest the need for language and culturally tailored public health and clinical initiatives to reduce cardiovascular risk in diverse populations.Ethn Dis.2019;29(2):287- 296; doi:10.18865/ed.29.2.287

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4

Mayes, Patricia. "Quotation in Spoken English." Studies in Language 14, no. 2 (January 1, 1990): 325–63. http://dx.doi.org/10.1075/sl.14.2.04may.

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5

Kolesnikova, Olga, and Oscar-Arturo González-González. "Spoken English Learner Corpora." Research in Computing Science 130, no. 1 (December 31, 2016): 111–32. http://dx.doi.org/10.13053/rcs-130-1-10.

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6

Calude, Andreea S., and Gerald P. Delahunty. "Inferentials in spoken English." Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA) 21, no. 3 (September 1, 2011): 307–40. http://dx.doi.org/10.1075/prag.21.3.02cal.

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Although there is a growing body of research on inferential sentences (Declerck 1992, Delahunty 1990, 1995, 2001, Koops 2007, Pusch 2006), most of this research has been on their forms and functions in written discourse. This has left a gap with regards to their range of structural properties and allowed disagreement over their analysis to linger without a conclusive resolution. Most accounts regard the inferential as a type of it-cleft (Declerck 1992, Delahunty 2001, Huddleston and Pullum 2002, Lambrecht 2001), while a few view it as an instance of extraposition (Collins 1991, Schmid 2009). More recently, Pusch’s work in Romance languages proposes the inferential is used as a discourse marker (2006, forthcoming). Based on a corpus study of examples from spoken New Zealand English, the current paper provides a detailed analysis of the formal and discoursal properties of several sub-types of inferentials (positive, negative, as if and like inferentials). We show that despite their apparent formal differences from the prototypical cleft, inferentials are nevertheless best analysed as a type of cleft, though this requires a minor reinterpretation of “cleft construction.” We show how similar the contextualized interpretations of clefts and inferentials are and how these are a function of their lexis and syntax.

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7

Gao, Chi. "The Spoken English Practice System Based on Computer English Speech Recognition Technology." Mobile Information Systems 2022 (April 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/9033421.

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Spoken English practice requires a combination of listening, speaking, reading, and writing, among which listening and speaking are the most difficult. In order to improve the speaking ability of the practitioner, the pronunciation of spoken English needs to be corrected in time. However, the workload of manual evaluation is too large, so it is necessary to combine intelligent methods for spoken language recognition. Based on the needs of spoken English pronunciation correction, this paper combines the computer English speech recognition technology to construct the spoken English recognition and correction model and combines the coding technology to study the English speech recognition technology. Moreover, this article constructs the spoken English practice system based on the actual needs of spoken English practice. Finally, this paper verifies the reliability of this system through experimental research, which provides a reliable means for the subsequent intelligent learning of spoken English.

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8

Peng, Yong Mei, and Yun Hua Qu. "Based on Research Connecting Word Corpus of Spoken English." Advanced Materials Research 1030-1032 (September 2014): 2689–92. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2689.

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This paper examines our spoken English Majors used to connect words and characteristics. Corpus used the "Chinese students Spoken and Written English Corpus (SWECCL2.0)" in the spoken corpus SECCL2.0, reference corpus used in the British National Corpus BNC spoken corpus BNC Spoken Corpus (BNC / S). The study found that of native speakers of English majors and English spoken words using both common connections are also differences. Meanwhile, China's English Majors spoken word there are multiple connections with the situation misuse. Based on the findings, the article on spoken English teaching some suggestions.

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9

Liu, Yang, and Jianguo Tian. "Error Analysis of College Students’ Spoken English." International Journal of Languages, Literature and Linguistics 7, no. 4 (December 2021): 182–88. http://dx.doi.org/10.18178/ijlll.2021.7.4.309.

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Based on the theory of Error Analysis, this thesis records the production of spoken English of first year college students of Northwestern Polytechnical University as samples. After listening to the recorder repeatedly and carefully, the author classifies and describes the errors found in the corpus, investigates the causes of these errors and provides solutions to these problems. It is found that there are errors of performance, phonological errors, lexical errors, grammatical errors and pragmatic errors in this study. Reasons for these errors can be explained from the perspectives of interlingual transfer, intralingual interference, cognitive and affective factors, and communicative strategies. Accordingly, some countermeasures could be taken to effectively decrease errors.

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10

Min, Sujung. "Comment Markers in Spoken English in Korea." English Teachers Association in Korea 30, no. 2 (June 30, 2024): 1–17. http://dx.doi.org/10.35828/etak.2024.30.2.1.

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The present study examines the use of four categories of comment markers among Korean speakers of English, a category of grammatical items whose use is prompted by pragmatic factors, using the data from the elicited conversation in English. Previous research on comment markers has suggested that comment markers are more frequent in the Inner Circle varieties than in the Outer Circle varieties and that there are systematic differences in the distribution of comment markers between Kachru’s Inner and Outer Circles. Since English is learned and used as an important foreign language in Korea, Korean society which belongs to the Expanding Circle is a suitable context for research on world Englishes and English as a lingua franca as well. Findings from the data analysis indicate that the use of comment markers among Korean speakers of English displays similar features with that of the Outer Circle varieties of English, especially East Asian varieties. Epistemic markers constitute the most sizable subcategory and the frequency of hearsay markers are relatively high raking the second in the data. The implications of the findings for the stylistic preferences and areal co-patterning are suggested. This paper contributes to the body of comment markers across the world Englishes.

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11

Wober, Mallory. "English - as she ain't spoken." British Journalism Review 8, no. 2 (June 1997): 64–67. http://dx.doi.org/10.1177/095647489700800210.

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12

Boberg, Charles. "Describing Spoken English: An Introduction." Journal of English Linguistics 28, no. 1 (March 2000): 96–99. http://dx.doi.org/10.1177/00754240022004811.

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13

Thomison, John B. "English As She Is Spoken." Southern Medical Journal 82, no. 9 (September 1989): 1069–70. http://dx.doi.org/10.1097/00007611-198909000-00001.

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14

Stewart, Ralph. "A decline in spoken English?" English Today 19, no. 3 (July 2003): 57–58. http://dx.doi.org/10.1017/s0266078403003110.

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“THIS IS THE CBC,” says the announcer. Till quite recently, stressing the “is” would have implied that someone had been asserting that it WASN'T the Canadian Broadcasting Company, but not now. Similarly, we are told that the Olympic games ARE taking place, tomorrow WILL be sunny, and the host of the program HAS convened the usual panel.

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15

Ciliberti, Anna. "Spoken English: A practical guide." Journal of Pragmatics 16, no. 6 (December 1991): 591–96. http://dx.doi.org/10.1016/0378-2166(91)90103-5.

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16

House, Jill. "Prosodic patterns in spoken English." Computer Speech & Language 2, no. 3-4 (September 1987): 364–67. http://dx.doi.org/10.1016/0885-2308(87)90019-2.

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17

Huang, Lihua. "An Improved BP Deep Neural Network Multimedia Used in Oral English Training." Scientific Programming 2022 (January 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/8995398.

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In order to improve the effect of spoken English training, this paper combines multimedia information technology to reform the teaching of spoken English training, and integrates BP neural network English into spoken English training. Moreover, this paper combines the actual needs of spoken English training and the teaching framework of the multimedia system to construct the data set, clean up the data set, and implement the word vector representation of students and professionals. In addition, this paper constructs the entire system framework of the spoken English resource recommendation algorithm based on the graph convolutional neural network, and combines the BP deep neural network algorithm to construct the spoken English training system. Finally, this paper designs an experiment to evaluate the effect of this system. The experimental research results show that the multimedia based on the BP deep neural network proposed in this paper has a good effect in the application research of spoken English training, and can effectively promote the effect of spoken English training of students.

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18

Tao, Hui, and Zhe Zhang. "Self-Study System Assessment of Spoken English considering the Speech Scientific Computing Knowledge Assessment Algorithm." Wireless Communications and Mobile Computing 2022 (May 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/3305499.

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For the purpose of effectively improving the assessment accuracy of spoken English self-study quality of English learners, a speech scientific computing knowledge assessment algorithm is introduced into the assessment of the self-study system of spoken English. Through the combination of spoken English speech assessment and accuracy detection, a DSP-based self-study assessment system for spoken English is designed. This system is mainly divided into two parts: voice signal processing and hardware circuit design. The features of spoken English self-study speech are extracted based on the multilayer wavelet feature conversion method, and the English pronunciation is detected and analyzed by adaptive filtering based on the characteristic values obtained. The corresponding wavelet entropy features of spoken English self-study speech are automatically assessed and assessed for the self-study quality. Finally, a practical case is analyzed, and the results indicate that the system proposed in this paper has high accuracy and excellent stability in assessing the quality of self-study in spoken English. Hence, it is of practical value in self-study assessment of spoken English.

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19

Teh, June Li, and Zahariah Pilus. "International students’ perspectives of Malaysian English teachers’ spoken English." Indonesian Journal of Applied Linguistics 8, no. 3 (January 31, 2019): 554. http://dx.doi.org/10.17509/ijal.v8i3.15255.

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Numerous studies comparing native and non-native English teachers have found that ESL students prefer native teachers for teaching speaking skills and pronunciation. In other words, non-native teachers are viewed as less superior in matters related to spoken language. This study explores international students’ views on spoken English of Malaysian teachers in English language classrooms. 81 international students who were attending English language classes as a preparation for university programmes at a Malaysian university participated in the study. The students were given a short writing task which required them to rate as well as stated their views on their Malaysian teachers’ spoken English in terms of speech rate, pronunciation, vocabulary, syntax, intelligibility, nativeness and acceptability for global communication. The study found that the international students considered the variety of Malaysian English used in the classroom as highly intelligible with high ratings for speech rates, vocabulary and sentence structures. Malaysian English is also viewed as highly acceptable for global communication. Although the teachers’ spoken language was rated lower for pronunciation and nativenesscompared to other traits confirming the views that non-native teachers are perceived as less proficient in pronunciation compared to the other skills, the ratings were still high indicating that in general, the acrolectal variety of Malaysian English as spoken in English classes is reasonably well accepted by other non-native speakers.

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20

Zhan, Xia. "A Convolutional Network-Based Intelligent Evaluation Algorithm for the Quality of Spoken English Pronunciation." Journal of Mathematics 2022 (January 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/7560033.

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Aiming at the problems of long time consumption and low accuracy of traditional spoken English pronunciation quality assessment algorithms, a convolutional network-based intelligent assessment algorithm for spoken English pronunciation quality is proposed. The convolutional neural network structure is given, the original data of the spoken English pronunciation voice signal are collected by multisensor detection, and the spoken English pronunciation voice signal model is constructed. Based on audio and convolutional neural network learning and training, it realizes the feature selection and classification recognition of spoken English pronunciation. The PID algorithm is used to extract the emotional elements of spoken English at different levels to achieve accurate assessment of the quality of spoken English pronunciation. The experimental results show that the average correct rate of spoken English pronunciation of the algorithm in this paper is 94.58%, the pronunciation quality score is 8.52–9.18, and the detection time of 100 phrases is 2.4 s.

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21

Bao, Shijun. "Design of Digital Teaching Platform for Spoken English Based on Virtual Reality." Advances in Multimedia 2022 (August 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/3274852.

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In order to improve the effect of digital teaching of spoken English, this paper combines virtual reality technology to design a digital teaching platform for spoken English and combines virtual reality technology to digitally process spoken English. The signal processed by the front-end amplifier circuit is converted into a digital signal through analog-to-digital conversion, and the digital receiving system is realized by means of software radio, which reduces the dependence on the hardware circuit and enhances the portability of the system. In addition, this paper selects filters according to the needs of digital teaching of spoken English and constructs a digital teaching platform for spoken English based on virtual reality. The experiment verifies that the digital teaching platform for spoken English based on virtual reality has a good effect of digital spoken English teaching.

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22

Jin, Yanju. "Design of Students’ Spoken English Pronunciation Training System Based on Computer VB Platform." International Journal of Emerging Technologies in Learning (iJET) 14, no. 06 (March 29, 2019): 41. http://dx.doi.org/10.3991/ijet.v14i06.10154.

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Spoken English communication is most commonly used in the international communication. However, the accuracy of spoken English pronunciation is the key factor to restrict English learners in China. For the current situation that spoken English proficiency is generally low in China, this paper aims to design a spoken English pronunciation training system that will provide guidance and help for English learners’ spoken pronunciation. The Visual Basic platform is used in the design of the system. This paper first conducts an in-depth study on the related theories of voice recognition, discusses the correction algorithm of voice scoring and pronunciation, and puts forward more practical and convenient AP-based scoring method, providing full theoretical support for the design of the system. Then through the function analysis and design of the spoken English pronunciation training system, this paper realizes the system design of scoring and correcting errors of English spoken pronunciation based on the VB platform. The system boasts the basic functions, including English phonetic symbols and word pronunciation to follow, real-time voice evaluation, and pronunciation error correction. According to the test, the similarity of the system with the experts is over 90% in scoring and its efficiency of pronunciation error correction reaches 80%, which plays a certain role in improving spoken English of English learners.

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23

Sadeghi, Karim, and Jack C. Richards. "Teaching spoken English in Iran’s private language schools: issues and options." English Teaching: Practice & Critique 14, no. 2 (September 7, 2015): 210–34. http://dx.doi.org/10.1108/etpc-03-2015-0019.

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Purpose – Mastery of spoken English is a priority for many learners of English in Iran. Opportunities to acquire spoken English through the public school system are very limited, hence many students enroll in “conversation” courses in private institutes. The purpose of this paper is to report a study of how institute teachers address the teaching of spoken English. Design/methodology/approach – Eighty-nine teachers completed a questionnaire on how they teach spoken English. The information was supplemented with interviews and classroom observation. Findings – Results suggest that institute courses reflect a poor understanding of the nature of spoken interaction, which is reflected in speaking courses that are unfocused and that do not address key aspects of conversational interaction. Practical implications – Suggestions are given for a re-examination of the differences between “conversation” and “discussion” in spoken English classes as the basis for designing spoken English classes and materials, as well as for the use of out-of-class learning opportunities to enhance the learning of spoken English in Iran and elsewhere. Originality/value – This paper is based on the authors’ original research, and the authors believe this is the first study of its kind in the Iranian context.

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24

Zhu, Min. "The Application of Intelligent Speech Analysis Technology in the Spoken English Language Learning Model." Mobile Information Systems 2022 (May 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/3192892.

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In order to improve the effect of spoken English processing, it is necessary to improve the spoken English processing technology from the perspective of the characteristics of spoken English, combined with intelligent algorithms. This paper combines the intelligent speech analysis technology to improve the spoken English recognition technology and combines the actual and needs of English learning to improve the system algorithm. Moreover, this paper combines the intelligent speech analysis to construct the intelligent spoken English learning model structure and combines the statistical method and the intelligent evaluation method to analyze the model effect. After obtaining the system function structure, this paper designs experiments to verify the effect of the model proposed in this paper. From the experimental analysis results, it can be seen that the intelligent English speech analysis model proposed in this paper can play an important role in the learning of spoken English.

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25

Johnson, Jeanne M., Ruth V. Watkins, and Mabel L. Rice. "Bimodal bilingual language development in a hearing child of deaf parents." Applied Psycholinguistics 13, no. 1 (January 1992): 31–52. http://dx.doi.org/10.1017/s0142716400005415.

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ABSTRACTThis study examined the spoken English development of a hearing child of deaf parents who used American Sign Language (ASL). The child first learned ASL in interactions with his parents and later developed spoken English outside the home environment. It was hypothesized that the child's acquisition of spoken English would systematically reflect both expected monolingual developmental patterns and interlinguistic transfer. Four areas of mismatch between ASL and spoken English were identified. Language sample data from ages 2;9–5;2 were examined for evidence to evaluate the hypotheses. Features that reflected the simultaneous versus sequential mismatch between ASL and English, undifferentiated versus differentiated aspects, free versus bound morpheme mechanisms, and word-order differences provided evidence of ASL influence on spoken English acquisition. Although not extensive, ASL appears to have exerted consistent influence on several areas of the child's spoken English development.

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26

Ren, Junlong. "Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory." Security and Communication Networks 2021 (December 27, 2021): 1–8. http://dx.doi.org/10.1155/2021/1486575.

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Aiming at the low confidence of traditional spoken English automatic evaluation methods, this study designs an automatic evaluation method of spoken English based on multimodal discourse analysis theory. This evaluation method uses sound sensors to collect spoken English pronunciation signals, decomposes the spoken English speech signals by multilayer wavelet feature scale transform, and carries out adaptive filter detection and spectrum analysis on spoken English speech signals according to the results of feature decomposition. Based on multimodal discourse analysis theory, this evaluation method can extract the automatic evaluation features of spoken English and automatically recognize the speech quality according to the results. The experimental results show that, compared with the control group, the designed evaluation method has obvious advantages in confidence evaluation and can solve the problem of low confidence of traditional oral automatic evaluation methods.

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27

Wang, Liyan, Jun Yang, Yongshan Wang, Yong Qi, Shuai Wang, and Jian Li. "Integrating Large Language Models (LLMs) and Deep Representations of Emotional Features for the Recognition and Evaluation of Emotions in Spoken English." Applied Sciences 14, no. 9 (April 23, 2024): 3543. http://dx.doi.org/10.3390/app14093543.

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This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation.

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Zhang, Menglin, Xiaoshu Xu, and YunFeng Zhang. "The Impact of Personal Learning Environments on Chinese Junior High School Students' Spoken English Narrative Competence." International Journal of Virtual and Personal Learning Environments 13, no. 1 (September 1, 2023): 1–23. http://dx.doi.org/10.4018/ijvple.329599.

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This study designed and implemented a personal learning environment platform for spoken English teaching in a junior high school in China to improve students' spoken English narrative competence. Altogether, 83 junior high school year one students took part in this study. The participants were divided into experimental and control classes for the two-month-long experiment. The personal learning environment-based spoken English platform was implemented in the experimental class, while the control class used traditional face-to-face instruction. Pre- and post-spoken English tests and semistructured interviews were used in the study. The data analysis revealed that PLEs has the potential to boost students' learning motivation and enthusiasm and improve their spoken English narrative performance as well. This study advanced practical research of PLEs in junior high school and enhanced the theoretical research in spoken English teaching.

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Han, Mengqing, and Shanshan Niu. "Application of Virtual Scenario Teaching in Spoken English Teaching." International Journal of Emerging Technologies in Learning (iJET) 16, no. 18 (September 20, 2021): 129. http://dx.doi.org/10.3991/ijet.v16i18.25659.

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The ultimate goal of learning the English language is oral communication. Virtual scenario teaching (VST) provides the speaking and listening opportunities for learners of spoken English, and allows students to actively participate in the creation of an English environment. Based on VST, this paper summarizes the status quo of spoken English teaching, applies VST to the teaching of spoken English, and evaluates the teaching effect. The results show that VST enhances the students’ ability of language expression, and stimulates their learning interest. With the aid of VST, students can master relevant knowledge, learn spoken English more fluently, and acquire better skills of oral expression. About 95% of teachers speak highly of VST. The research results lay a theoretical basis for reforming and improving the teaching of spoken English.

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30

Wang, Yongjuan, and Peng Zhao. "A Probe into Spoken English Recognition in English Education Based on Computer-Aided Comprehensive Analysis." International Journal of Emerging Technologies in Learning (iJET) 15, no. 03 (February 18, 2020): 223. http://dx.doi.org/10.3991/ijet.v15i03.12937.

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At present, computer-aided spoken English learning is becoming increasingly popular among learners. The computer-aided comprehensive analysis tech-nology can evaluate and correct learner's spoken pronunciation, thereby im-proving their pronunciation. Based on computer-aided comprehensive analy-sis, this paper aims to explore the automatic recognition and scoring methods of spoken English in English education. For this, it studies the effective matching of the feedback information with the known pronunciation scoring results, and then develops a computer evaluation plug-in consisting of dif-ferent modules such as user login, English spoken speech acquisition and recognition, voice evaluation, speech broadcast, and spoken dialogue. The research results show that the computer evaluation plug-in matches and compares the extracted feature parameters of input speech with the standard features, scores the spoken language input by the learner, and gives the cor-rect pronunciation so that the learner can get feedback in time. For different stages of English learning, the focus of recognition technology and the spo-ken recognition algorithms applied also vary. The research findings provide theoretical and technical support for oral English recognition, error correction and scoring.

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31

H. Narendra Kumar, M. Tilak,, B. Mohan Teja,, and S. Santha Kumari. "New Challenges In Teaching Spoken English For Employment At The Undergraduate Level." Journal of Namibian Studies : History Politics Culture 33 (May 16, 2023): 3258–67. http://dx.doi.org/10.59670/jns.v33i.5079.

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The employment sector in India is diversifying with the liberalization of the economy. Education at the undergraduate level is however at present, not in tune with the requirements of the job market. In order to provide employment related skills to the undergraduate students, more stress needs to be placed on linking education with work. This is applicable to the teaching of Spoken English also. This paper addresses the need to give more importance to the development of the speaking skills in English, which is the most neglected skill among the language skills taught at the undergraduate level. The paper examines the following: The notion of spoken English as it is taught in the undergraduate level. The nature of spoken English taught through the existing English courses at the undergraduate level. The scope of spoken English taught through the option vocation-oriented Functional English course 4. The work-related skills that need to be linked with the teaching of spoken English at the undergraduate level Based on the observation: a three stage approach for co-ordinating the efforts to relate spoken English to employment needs is suggested.

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32

Jiang, Zhaoxia. "Spoken English Assessment Using Confused Phoneme Assessment Model." Mobile Information Systems 2021 (May 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/2574332.

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Under normal circumstances, in the process of evaluating the phoneme of spoken English which is easy to be confused, it is impossible to accurately assess the oral ability of different groups of people according to their oral ability. The assessment process has problems of poor robustness and low stability. We propose a spoken English assessment method based on an easily confused phoneme assessment model to address these problems. We design an English easily confused phoneme-based evaluation model in the proposed framework by adopting fuzzy logic for the assessment task. We also present HPD set for confused phonemes and introduce the easily confused phonemes in spoken English. Moreover, we derive four fuzzy measure assessment grades of E/G/NI/GR and present the assessment model for them. We continuously recognize and annotate spoken English to find the best-matched statement and complete the recognition and assessment of easily confused phoneme. Then, we also focus on spoken English assessment based on an easily confused phoneme assessment model. Empirical results demonstrate the superior performance of our proposed models over the conventional evaluation methods. Our proposed models improve the spoken English assessment method by 30% and the stability by 45%. Besides, our model is also suitable for the spoken English assessment of different groups of people.

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33

Liu, Yijun. "Differences between Spoken and Written English." Communications in Humanities Research 3, no. 1 (May 17, 2023): 766–70. http://dx.doi.org/10.54254/2753-7064/3/20220615.

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The link between spoken and written language has been the subject of many diverse perspectives throughout the history of language research. Many linguists considered written language a variant of spoken language in the past few decades. But over time, written language has come to hold a dominant position. Therefore, this paper reviews the differences between the two types of English language, spoken and written English, by analyzing the features of these two types of English through three aspects: production and perception, forms, and formality. This review also provides some pedagogical suggestions for writing and speaking instructors who want to engage ESL pupils better.

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Rayah, Rabiatul Adawiyah, Nurul Aiman Mohd Jafre, Nor Aini Husain, and Wan Rosedayu Wan Mohamad Nawawi. "Attitude and motivation in spoken English." Jurnal EDUCATIO: Jurnal Pendidikan Indonesia 4, no. 2 (December 15, 2018): 100. http://dx.doi.org/10.29210/120182256.

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<p><em>English language proficient has currently becoming a necessary requirement in various fields of study and professions. However, most students refuse to speak in English because they are too concerned in making mistakes, lack of motivation, and low self-confidence. In learning English, attitude and motivation are very closely related. A learner with positive attitude towards learning a language has a high tendency of motivation towards it. Therefore, the main objective of the research was to investigate the correlation between the items of attitude and motivation in spoken English among semester 4 students at Politeknik Sultan Mizan Zainal Abidin (PSMZA). The methodology used was quantitative method. This study was carried out using a questionnaire consisted of 70 items adapted from Gardner’s Attitude/Motivation Test Battery (AMTB) and was distributed to 100 semester 4 students from four departments of PSMZA. The data was analyzed using SPSS. Cohen’s standard was used to evaluate the correlation coefficient, where items with 0.50 or larger were highlighted which represented a strong association. Findings had shown these students’ attitude and motivation towards spoken English had strong correlation with some of the items that helped to boost their motivation in spoken English. These findings indicated the importance of different strategies to be implemented by the English language educators to enhance students’ motivation in communicative English subjects.</em></p>

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Renwick, Margaret E., and Caitlin N. Cassidy. "Detecting palatalization in spontaneous spoken English." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2267. http://dx.doi.org/10.1121/1.4920277.

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36

Brownlees, Nicholas. "Spoken Discourse in Early English Newspapers." Media History 11, no. 1-2 (April 2005): 69–85. http://dx.doi.org/10.1080/1368880052000342424.

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Nesselhauf, Nadja, and Ute Römer. "Lexical-grammatical patterns in spoken English." International Journal of Corpus Linguistics 12, no. 3 (September 19, 2007): 297–333. http://dx.doi.org/10.1075/ijcl.12.3.02nes.

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Based on a large set of data from one of the biggest available corpora of spoken British English (the 10-million word spoken component of the BNC), this article explores central lexical-grammatical aspects of progressive forms with future time reference. Among the phenomena investigated are verb preferences, adverbial co-selection, subject types, and negation. It is demonstrated that future time progressives in spoken British English are patterned to a considerable extent (for example that it is individual verbs, rather than semantic groups of verbs, that preferably occur in such constructions) and that actual language use often runs counter to claims that can be found in traditional grammatical descriptions of the construction. A number of general and often neglected issues in the analysis of lexical-grammatical patterns are also addressed, in particular the notion of pattern frequency.

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Laws, Jacqueline, and Chris Ryder. "Register variation in spoken British English." International Journal of Corpus Linguistics 23, no. 1 (May 31, 2018): 1–27. http://dx.doi.org/10.1075/ijcl.16036.law.

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Abstract The aim of this paper is to identify the effect of register variation in spoken British English on the occurrence of the four principal verb-forming suffixes: ‑ate, ‑en, ‑ify and ‑ize, by building on the work of Biber et al. (1999), Plag et al. (1999) and Schmid (2011). Register variation effects were compared between the less formal Demographically-Sampled and the more formal Context-Governed components of the original 1994 version of the British National Corpus. The pattern of ‑ize derivatives revealed the most marked register-based differences with respect to frequency counts and the creation of neologisms, whereas ‑en derivatives varied the least compared with the other three suffixes. Quantitative and qualitative analyses of these suffix profiles in the context of spoken language reveal markers of register formality that have not hitherto been explored; derivative usage patterns provide an additional dimension to previous research on register variation which has mainly focused on grammatical and lexical features of language.

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39

Alexander, James D. "The stress factor in spoken English." English Today 2, no. 1 (January 1986): 31–34. http://dx.doi.org/10.1017/s026607840000170x.

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Like Russian and Arabic but unlike French and Japanese, spoken English is a flow of stronger and weaker syllables. Usually the stronger syllables are ‘obvious’, but sometimes they are not – and foreign learners always have problems. The stress patterns of English are complex – they shift with the passing of the years and are by no means the same everywhere.

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40

Dressler, Wolfgang U., and Katarzyna Dziubalska-Kolaczyk. "Sound Patterns of Spoken English (review)." Language 81, no. 1 (2005): 274–76. http://dx.doi.org/10.1353/lan.2005.0014.

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41

Gráf, Tomáš. "Verb Errors in Advanced Spoken English." AUC PHILOLOGICA 2017, no. 1 (July 20, 2017): 131–49. http://dx.doi.org/10.14712/24646830.2017.8.

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42

Powell, Robert G. "Measuring minimal proficiency in spoken English." Communication Reports 3, no. 1 (January 1990): 37–44. http://dx.doi.org/10.1080/08934219009367499.

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43

Park, Myungkwan. "On gapless relatives in spoken English." Language and Linguistics 103 (February 28, 2024): 29–52. http://dx.doi.org/10.20865/202410302.

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44

Ridwan, Rina Indrianty, Ahmad Munir, and Oikurema Purwati. "THE PERCEPTION ABOUT DIFFICULTIES OF DIALECT BUGINESE TOWARD STUDENT’S SPEAKING IN MUHAMMADIYAH UNIVERSITY OF MAKASSAR." EXPOSURE : JURNAL PENDIDIKAN BAHASA INGGRIS 11, no. 1 (June 2, 2022): 100–115. http://dx.doi.org/10.26618/exposure.v11i1.6732.

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The difficulty of speaking English, especially for first graders It will be a sustainable issue. Seeing that phenomenon, this study aimed to see difficulty in speaking English among first graders of English education Muhammadiyah University of Makassar. There are two in this study. This study takes a qualitative approach of choosing a location and choosing Students will batch 2020 as a participant in this study. Participants were selected through recommendations from all instructors who taught the course. About difficulty in speaking English, 4 difficulties in this study. The result was Interference from students' dialect according to the research results, the dominant the interference to students‟ spoken language is interfere with pronunciation stress any part of the word. Student dialect factors difficulties speaking English (internal factor = self-confidence) main distractions to students performance was an interference with spoken language performance lasses that make students more confident, those students who felt wrong in the lecture English and their friends can’t understand because they spoke English was like they speak the local dialect, it interferes the motivation of the student to speak.

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Love, Robbie, Claire Dembry, Andrew Hardie, Vaclav Brezina, and Tony McEnery. "The Spoken BNC2014." International Journal of Corpus Linguistics 22, no. 3 (November 23, 2017): 319–44. http://dx.doi.org/10.1075/ijcl.22.3.02lov.

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Abstract This paper introduces the Spoken British National Corpus 2014, an 11.5-million-word corpus of orthographically transcribed conversations among L1 speakers of British English from across the UK, recorded in the years 2012–2016. After showing that a survey of the recent history of corpora of spoken British English justifies the compilation of this new corpus, we describe the main stages of the Spoken BNC2014’s creation: design, data and metadata collection, transcription, XML encoding, and annotation. In doing so we aim to (i) encourage users of the corpus to approach the data with sensitivity to the many methodological issues we identified and attempted to overcome while compiling the Spoken BNC2014, and (ii) inform (future) compilers of spoken corpora of the innovations we implemented to attempt to make the construction of corpora representing spontaneous speech in informal contexts more tractable, both logistically and practically, than in the past.

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46

Wang, Shuli, and Xiuchuan Shi. "Research on Correction Method of Spoken Pronunciation Accuracy of AI Virtual English Reading." Advances in Multimedia 2021 (December 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/6783205.

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In order to improve the pronunciation accuracy of spoken English reading, this paper combines artificial intelligence technology to construct a correction model of the spoken pronunciation accuracy of AI virtual English reading. Moreover, this paper analyzes the process of speech synthesis with intelligent speech technology, proposes a statistical parametric speech based on hidden Markov chains, and improves the system algorithm to make it an intelligent algorithm that meets the requirements of the correction system of spoken pronunciation accuracy of AI virtual English reading. Finally, this paper combines the simulation research to analyze the English reading, spoken pronunciation, and pronunciation correction of the intelligent system. From the experimental research results, the correction system of spoken pronunciation accuracy of AI virtual English reading proposed in this paper basically meets the basic needs of this paper to build a system.

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47

Genç-Yöntem, Ece, and Evrim Eveyik-Aydın. "The compilation of a developmental spoken English corpus of Turkish EFL learners." Research in Corpus Linguistics 10, no. 1 (2021): 45–62. http://dx.doi.org/10.32714/ricl.10.01.03.

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Although compiling a spoken learner corpus is not a recent enterprise, the number of developmental learner spoken corpora in the field of corpus linguistics is not satisfactory. This report describes the compilation of the Yeditepe Spoken Corpus of Learner English (YESCOLE), a 119,787-word corpus of Turkish students’ spoken English at tertiary level. YESCOLE was compiled to generate a developmental corpus of spoken interlanguage by collecting samples from learners of different English proficiency levels at regular short intervals over seven months. In order to shed light on the laborious methodology of compiling the developmental spoken learner corpus, this paper elucidates the steps taken to build YESCOLE and discusses its potential benefits for research and instructional purposes.

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Choomthong, Daranee, and Supaporn Manowong. "Varieties of English Accents: A Study of the Degree of Preference and Intelligibility Among Second-Year English Major Students at Maejo University." Manusya: Journal of Humanities 23, no. 2 (August 5, 2020): 151–69. http://dx.doi.org/10.1163/26659077-02302001.

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Thailand is regarded as a country of the expanding circle (EC). The fact that English has become a working language in the asean community makes it vital that Thai students are aware of the varieties of English. The study examined the perception of English majors towards varieties of English pronunciation. Listening tasks spoken by speakers in the expanding circle (EC), the inner circle (IC) and outer circle (OC), were presented to students enrolled in a course on Sound and English Sound System. The students rated accent preference and intelligibility. A semi-structure interview was included for more in-depth information. The results revealed that the variety of English that was perceived as the most favorable accent by the participants was English spoken by speakers from IC. The participants were more aware of varieties of English, especially those spoken by non-native speakers of English. However, English spoken by speakers from the EC was perceived as the most intelligible.

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Wang, Xue. "Research on Oral English Learning System Integrating AI Speech Data Recognition and Speech Quality Evaluation Algorithm." Journal of Electrical Systems 20, no. 5s (April 13, 2024): 2466–77. http://dx.doi.org/10.52783/jes.2688.

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The application of AI speech data recognition (SDR) and speech quality assessment algorithms in spoken English learning systems can help to realize the intelligence and automation of the process related to spoken English learning. To better assist the people concerned with the learning of spoken English, it is necessary to integrate AI speech data recognition technology in the process of learning spoken English to bring learners a learning environment and learning conditions that are not bound by time and space. As an important way to realize the natural communication of information between machines and people, AI speech recognition technology has significant application advantages and application values in eliminating the influence of the native language of English speaking learners and innovating the learning mode in the local area. In addition, the assessment of the reliability of spoken English language learning can be carried out in several dimensions. However, it is still difficult to unify the criteria of many sizes, bringing new application scenarios for AI SDR. Based on this, this paper first analyzes the theoretical basis of AI SDR. Then it investigates the speech data recognition method of the fused AI English-speaking learning mechanism and the process of fused AI English-speaking speech quality assessment. Finally, it designs and verifies the functions of the spoken English learning mechanism incorporating AI speech data identification and quality assessment.

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Джумамбетова, Дилфуза. "Cleft Sentences In Spoken Language." Актуальные вопросы лингвистики и преподавания иностранных языков: достижения и инновации 1, no. 1 (April 24, 2024): 207–9. http://dx.doi.org/10.47689/topical-tiltfl-vol1-iss1-2024-pp207-209.

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In this article, we will explore the concept of cleft sentences, a complex speaking structure commonly used in English. Cleft sentences are a powerful tool for emphasizing information or correcting someone in a sentence. Understanding the different types of cleft sentences and their variations will help you master this aspect of English speaking.

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