Category Archive : Band Score

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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Developing E-learning Skills Within English Language Training

Developing E-learning Skills Within English Language Training

Foreign language training at the Slovak University of Technology (hereinafter referred as STU), the Faculty of Materials Science and Technology (MTF), Institute of Engineering Pedagogy and Humanities (ÚIPH), Department of Professional Language Training (KOJP) is being carried out according to the syllabus based on a thorough analysis of the Faculty’s students and graduates’ needs on one hand, and the demands of practice and employers on the other hand. The syllabus has been continuously innovated to improve the graduates’ skills and their competitiveness in the regional and international job markets, while satisfying also the demands of industrial practice. Developing E-learning Skills Within English Language Training

The innovations reflect the challenges of the Lisbon strategy, Sorbonne declaration and Bologna declaration: to improve foreign language competence of the students of technical branches, to increase their readiness to enter the European job market, and to harmonise the European university sectors in order to prepare graduates for mobility, global job market and global and international research and scientific environment.

Since the integration into the European Union, the STU students have encounter the challenges to participate in student mobility in foreign universities in the countries, including Germany, Russia, Spain, Finland, Belgium and Japan, where e-learning practices are a prerequisite for mastering the university study. At the same time, the Slovak Republic has attracted foreign investors and multinationals. In wider context, it can be considered as forming positive attitudes e.g. to entrepreneurship as well as building necessary entrepreneurial skills, e.g. initiative, creativity, willingness to risk, reliability, etc. [1]. This consequently calls for developing creative and challenging e-education environment and developing e-learning skills within English for Science and Technology (EST) language training.

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Training College Students’ Comprehensive Qualities by English Class Lectures

Training College Students’ Comprehensive Qualities by English Class Lectures

College English Curriculum Requirements points out that English teaching objective is to develop students’ English language proficiency, especially listening and speaking skills so that they can communicate in English effectively in their future study, work and social interactions, meanwhile to enhance their autonomy ability, improve their overall cultural quality to meet needs of China’s social development and international communication Training College Students’ Comprehensive Qualities by English Class Lectures

College students have learned English for at least six years before they enter the university, but generally their listening and speaking abilities are weak, which requires a proper environment for them to practice in the limited class time. The research group introduces English speech activities into the class based on Output Hypothesis and College English Curriculum Requirements. The students practice their oral English by lectures in classes.

In order to improve students’ English proficiency, since 2008 our reach group has carried out one two-year experiment. For the experimental class, in the first ten minutes students are asked to give short English lectures in the different stages, first prepared speech, and later semiprepared speech, impromptu speech last. Presentation includes related questions from teachers and other students who make recommendations based on the performance of the speaker. After two years of experiments, questionnaire result in the experimental class shows that among 36 students 34 felt that their English proficiency improved, 27 of them had a more substantial increase. Only 2 did remain at the same level. About 94% students in the experimental class improved their English application abilities. In the experimental class, 31 students improved their learning ability, 34 improved language application capabilities, 33 improved their ability to analyze problems, 35 improved adaptability, 32 improved innovation ability. The research group made the sub-trace analysis over the 2 years scores of the experimental class, from which we drew a conclusion that there are significantly differences between the experimental classes scores and control classes scores compared to their similar entrance scores after 2 years’ experiments. That is to say, after 2 years of class lecture experiment, students’ English scores improved significantly.

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AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH

AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH

The concept of contrast plays an important role in many spo-ken language technologies, ranging from spoken language un-derstanding to speech synthesis. According to the observationpoint one looks at it, contrast can be seen as: a) a discourserelation that ties discourse elements; b) a concept of infor-mation structure that makes a word (or a phrase) salient bycomparing it with other word(s) available from the discoursecontext; c) a linguistic concept often prosodically marked.Given the broad meaning of contrast, the different dis-course scenarios invoking it, the poor availability of corporaannotated with categories of contrast, and our main researchinterest of investigating the role of contrast in prosodic promi-nence modeling for text-to-speech applications, we decided tofocus on one aspect/category of contrast only: an informationstructure relation that links two semantically related wordsthat explicitly contrast with each other.

Before merging the syntactic and the information structureannotations we converted the constituent format in the PennTreebank into dependency trees using the Penn2Malt con-verter ([6]). Since the PennTreebank constituent annotationfor Switchboard uses slightly different (and not yet standardlyheld) conventions from whose presupposed by the Penn2Maltconverter we had to support the converter with some addi-tional scripts. However, because of problems we encounteredin the conversion process we had to remove 54 (out of 146)dialogues. For each remaining dialogue all the word senses(according to the WordNet senses set) were disambiguatedusing the WordNet::SenseRelate Perl module ([7]).

All syntactic features are POS, dependency relations (subjectof, object of, etc…) and features derived from both of them.Examples of features derived from POS are the features indi-cating if W1 is the only word in the sentence having the samebroad POS of W2, and the feature indicating if W1 is the clos-est (in term of words between them) word preceding W2 andhaving the same broad POS. The use of deeper than POS syntactic information suchas syntactic dependencies (and information related to them)is motivated by the need of identifying syntactic patterns ofcontrastiveness that can not be identified using POS and lex-ical features alone. For example knowing that W1 and W2have the same type of dependency with their heads as in ex-ample (3) (both “you” and the first “I” have a “subject of”dependency with “take” and “do” respectively) or that theirheads refer to the same item as in example (6), seems to bea necessary (but often not sufficient) information to identifycontrast. https://speakinenglish.in/

 AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH
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