Tag Archive : Ielts Material For Speaking Writing Listening

Relationship between Perception and Production of English Vowels by Chinese English Learners

Relationship between Perception and Production of English Vowels by Chinese English Learners

In previous studies, no consensus has been reached on the existence of significant correlation between perception and production. A large number of empirical studies have been done upon first and second languages from different language families. However, few studies were carried out on the perception-production relation of Chinese English learners. Therefore, in the current study, under the theoretical framework of PAM-L2, 40 subjects with even numbers in two genders, who differ in language proficiency, are invited to participate in the perception experiment and the production experiment, in which discrimination, identification and pronunciation of /ɪ/-/ε/, /ε/-/æ/, /ʊ/-/ʌ/, and /ʌ/-/ɒ/ contrasts are observed. Results reveal that vowel perception of Chinese English learners is neither statistically correlated nor spectrally related to vowel production. Relationship between Perception and Production of English Vowels by Chinese English Learners

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As foreign language teaching develops worldwide, scholars in second language (L2) learning and acquisition unanimously found a vague link between “listening” and “speaking”. Spoken proficiency of L2 learners was improved even though they didn’t receive training in pronunciation but increased exposure to native production [1]. Scholars began to consider whether there was a close bond between perception and production. If there was, in L2 education, teachers could not only concentrate their training on production, but also add some training to the perception of L1 sounds. Training of perception could also be applied as a supplementary method in phoniatric training for those who failed to adjust their pronunciation merely by articulation training such as imitation.

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TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

This study proposes a new two-layer approach for spoken language translation. First, we develop translated examples and transform them into speech signals. Second, to properly retrieve a translated example by analyzing speech signals, we expand the translated example into two layers: an intention layer and an object layer. The intention layer is used to examine intention similarity between the speech input and the translated example. The object layer is used to identify the objective components of the examined intention. Experiments were conducted with the languages of Chinese and English. The results revealed that our proposed approach achieves about 86% and 76% understandable translation rate for Chinese-to English and English-to-Chinese translations, respectively. TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

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With the growing of globalization, people now often meet and do business with those who speak different languages, on-demand spoken language translation (SLT) has become increasingly important (See JANUS 111 [I], Verbmobil [2], EUTRANS [3], and ATR-MATRIX [4]). Recently, an integrated architecture based on stochastic finite-state transducer (SFST) has been presented for SLT [3,5]. The SFST approach integrated three models in a single network where the search process takes place. The three models are Hidden Markov Models for the acoustic part, language models for the source language and finite state transducers for the transfer between the source and target language. The output of this search process is the target word sequence associated to the optimal path. Fig. 1 shows an example of the SFST approach. The source sentence ‘‘una habitacidn doble” can he translated to either “a double room” or “a room with two beds”. The most probable translation is the first one with probability of 0.09.

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TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

A high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored. TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

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There is a high demand around the world for the learning of English as a second language. Assessment of a learner’s language proficiency is a key part of learning both in measuring progress made and for formal qualifications required e.g. for entrance to university or to obtain a job. Given the high demand from English learners, it will be very difficult to train sufficient examiners and the introduction of automatic markers will be beneficial especially for practice situations.

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AUTOMATIC GRAMMATICAL ERROR DETECTION OF NON-NATIVE SPOKEN LEARNER ENGLISH

AUTOMATIC GRAMMATICAL ERROR DETECTION OF NON-NATIVE SPOKEN LEARNER ENGLISH

Automatic language assessment and learning systems are required to support the global growth in English language learning. They need to be able to provide reliable and meaningful feedback to help learners develop their skills. This paper considers the question of detecting “grammatical” errors in non-native spoken English as a first step to providing feedback on a learner’s use of the language. A stateof-the-art deep learning based grammatical error detection (GED) system designed for written texts is investigated on free speaking tasks across the full range of proficiency grades with a mix of first languages (L1s). This presents a number of challenges. Free speech contains disfluencies that disrupt the spoken language flow but are not grammatical errors. The lower the level of the learner the more these both will occur which makes the underlying task of automatic transcription harder. The baseline written GED system is seen to perform less well on manually transcribed spoken language. When the GED model is fine-tuned to free speech data from the target domain the spoken system is able to match the written performance. Given the current state-of-the-art in ASR, however, and the ability to detect disfluencies grammatical error feedback from automated transcriptions remains a challenge. AUTOMATIC GRAMMATICAL ERROR DETECTION OF NON-NATIVE SPOKEN LEARNER ENGLISH

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Automatic systems that enable assessment and feedback of learners of a language are becoming increasingly popular. One important aspect of these systems is to provide reliable, meaningful feedback to learners on errors they are making. This feedback can then be used independently, or under the supervision of a teacher, by the learner to improve their proficiency. A growing number of applications are available to non-native learners to improve their English speaking skills by providing feedback on aspects such as pronunciation and fluency.

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English for Spoken Programming

English for Spoken Programming

Existing commercial and open source speech recognition engines do not come with pre-built models that lend themselves to natural input of programming languages. Prior approaches to this problem have largely concentrated on developing spoken syntax for existing programming languages. In this paper, we instead describe a new programming language and environment that is being developed to use “closer to English” syntax. In addition to providing a more intuitive spoken syntax for users, this allows existing speech recognizers to achieve improved accuracy using their pre-built English models. Our basic recognizer is built from a standard context-free grammar together with the CMU Sphinx pre-trained English models. To improve its accuracy, we modify the language model during runtime by factoring in additional context derived from the program text, such as variable scoping and type inference. While still a work in progress, we anticipate that this will yield measurable improvements in speed and accuracy of spoken program dictation. English for Spoken Programming

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The dominant paradigm for programming a computer today is text entry via keyboard and mouse. Keyboard-based entry has served us well for decades, but it is not ideal in all situations. People may have many reasons to wish for usable alternative input methods, ranging from disabilities or injuries to naturalness of input. For example, a person with a wrist or hand injury may find herself entirely unable to type, but with no impairment to her thinking abilities or desire to program. What a frustrating combination!

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DOES TRAINING IN SPEECH PERCEPTION MODIFY SPEECH PRODUCTION

DOES TRAINING IN SPEECH PERCEPTION MODIFY SPEECH PRODUCTION

To examine the relationship between speech perception and production in second language acquisition, this study investigated whethex training in the perception domain transfers to improvement in the production domain. Native speakers of Japanese wen trained to identifi English lrl-N minimal pairs. Recordings were made of the subjects’ productions of minimal pairs before and after identification training. American-English listeners then perceptually evaluated these productions. The subjects showed significant improvements from pretest to post-test in perception as well as in production. Furthermore, the subjects retained these abilities in follow-up tests given three months and six months after the conclusion of mining. These results demonstrate that training m the perception domain produces long-tmn modifications in both perception and production, implying a close link between speech perception and production. DOES TRAINING IN SPEECH PERCEPTION MODIFY SPEECH PRODUCTION

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The relationship between speech Perception and production has been a long-standing issue in second language (L2) acquisition as well as in native language (L1) acquisition. It is well-known that some phonetic contrasts in one language are difficult for speakers of another language to perceive and produce. For example, the English /r/4 contrast is remarkably difficult for Japanese speakers to perceive and produce even after many years of education in English as an L2, or immersion in an English-speaking environment. Yamada et al. (1994 161) have shown a significant correlation between perception acwacy and production intelligibility of English Irl-N tokens by Japanese speakers. This result implies a link between perception and production in L2 acquisition. However, few studies have examined this perception-production link directly. One way of addressing this issue is to investigate the effects of artificial changes in one domain, either perception or production, on the other domain.

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Oral assessment applied to a literature course for English language teaching students

Oral assessment applied to a literature course for English language teaching students

This study refers to some positive and negative effects of oral assessment applied to a literature course. The experiment was conducted in a north Cyprus university English language teaching (ELT) department, during the spring semester of the 2016-2017 academic year. The data were collected from the ELT students taking the Survey to English Literature course (SELC) during the spring semester of the 2016-2017 academic year, who were asked to fill in an open ended questionnaire about the intervention, and semi structured interviews conducted with the co-examiners. In the ELT department under discussion, almost all the exams are written, and the students do not have too many opportunities to speak during the courses or during their mid-term exam. The results show the benefits and drawbacks of implementing oral examinations to assess students’ literature knowledge. Oral assessment applied to a literature course for English language teaching students

Teaching English as a foreign language (EFL) students to teach EFL is a double folded endeavor. On the one hand it aims at improving students’ level of English; on the other hand it seeks to prepare them to use modern, appropriate methods and techniques to teach it. The syllabus used by the ELT department under discussion includes a SELC, which is taught for two semesters during the second year of studies. The assessment of the course had only been done through a written format before the implementation of this intervention. The present study followed the case study procedure. Twenty students and two coexaminers were the participants in the study. The students were exposed to an intervention for six weeks, which included an oral exam assessed by two co-examiners and the researcher. Further on, the students were asked to fill in an open-ended questionnaire in which their opinions were required. The co-examiners took part in a semi-structured interview about their opinions concerning the oral exam. The data obtained was qualitatively interpreted and the results showed the effects of the intervention.

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Analysis of English pronunciation of voices

Analysis of English pronunciation of voices

Singing songs is one of the most popular amusements in Japan. We sing many kinds of songs at occasions such as karaoke. However, it is difficult for most of Japanese native speakers to sing English songs because of difference of phone inventory of the two languages. Nowadays, there are numerous studies of CALL (Computer Assisted Language Learning) systems including the training of English pronunciation; however, there is no system that evaluates English pronunciation of the sung English. We are now investigating how to develop such a system by analyzing English singing voice and the result of subjective evaluation. In this paper, we show the result of the subjective evaluation as well as the analysis results. As a result, we found that not only the number of mispronunciations but also other factors affect the perceived goodness of English pronunciation. We also found that pronunciation scores of the singing voice by singers with singing experience were higher than that of spoken speech, which might mean that the experience of singing improves the skill of English singing .

Analysis of English pronunciation of voices

In these several decades, English songs have become popular among Japanese due to the development of communication technology such as radio broadcast, TV and the Internet. Nowadays, we can watch various music videos and buy favorite songs anytime. It causes not only promoting the Western music, but also raising the frequency that English was used in Japanese pop music. However, English pronunciation may be a problem when a Japanese sings songs with English lyrics. There are remarkable linguistic differences between Japanese and English [1]. These differences become an obstacle for a Japanese to learn English, and most Japanese feel it hard to speak in English with correct pronunciation. Thus, there have been a number of researches for improving English skills from speech processing point of view. These works developed Computer-Assisted Language Learning (CALL) systems that include functions for training of oral communication, such as pronunciation evaluation

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Adapting Training to International Standards: A Case Study in Aviation English Training

Adapting Training to International Standards: A Case Study in Aviation English Training

The International Civil Aviation Organization (ICAO) has mandated that pilots and air traffic controllers around the world meet or exceed a set standard of English Language Proficiency by 2008. This paper presents a case study of an English language training company’s adaptation of its Aviation English training program to match ICAO goals and to help clients meet the mandated level of proficiency.

While developing and delivering training to an international audience is always challenging, undertaking this task to help multinational clients meet an international professional standard can be daunting. While the case study presented in this paper deals with English language training within the field of aviation, the same review and adaptation process could apply to all training companies, especially those that deal with high stakes training – for example, within the medical arena – and/or those whose training content is overseen or certified by professional or governmental regulatory agencies.

Adapting Training to International Standards: A Case Study in Aviation English Training

In the current case study the professionals being trained are non-native English speaking air traffic controllers (ATCs) and commercial airline pilots. The standard for them to reach is a newly mandated level of English language proficiency, especially in the area of radiotelephony, the specialized system of terminology and phraseology used by pilots and air traffic controllers to communicate during takeoff, flight, and landing.

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Innovation in Secondary School English Teaching

Innovation in Secondary School English Teaching

Secondary school English teaching plays a key role in English education in China, while its current status, especially in the countryside, is not as good as we expected and thus innovation in this field becomes in dispensible. This paper analyses a questionnaire research statistics with SPSS 16.0 and tracks down the barriers to innovation in secondary school English teaching in the countryside of China and puts forward some innovative strategies in this field, such as the innovations in management and training programs .

Innovation in Secondary School English Teaching

So far, issues involving innovation in teacher training have been studied by education researchers, reformers, and practitioners in the whole world, for example, Lilly [1] , Cruz, B. [2], Clapham, M. M. [3], Xie Anbang [4], and etc. The word “innovate” can be traced back to the 1400s, where it originated from the word “innovacyon” meaning “renewal” or “new way of doing things”. Innovation has been defined as “the challenging of creativity so as to produce a creative idea and/or product that can and wish to be used” [5]. Specifically, innovation in teaching can involve innovative curricular, instructional, and management strategies that will effectively benefit the classes and may be shared by colleagues.

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