ECSIELTS

How to Listening Test and Spoken BEC

A user can scan through a text easily, but it is not the case for spoken content, because they cannot be directly displayed on-screen. As a result, accessing large collections of spoken con-tent is much more difficult and time-consuming than doing so for the text content. It would therefore be helpful to develop machines that understand spoken content. In this paper, we propose two new tasks for machine comprehension of spoken content. The first is a listening comprehension test for BEC, a challenging academic English examination for English learners who are not the native English speakers. How to Listening Test and Spoken BEC

We show that the proposed model out performs the naive approaches and other neural network based models by exploiting the hierarchical structures of natural languages and the selective power of attention mechanism. For the second listening comprehension task – spoken squad we find that speech recognition errors severely impair machine comprehension; we propose the use of sub word units to mitigate the impact of these errors.

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How to PTE Scores to Predict in SPEAK Test

The PTE english test , produced by the Educational Testing Service (ETS), has been in use in institutions of higher American education since the 1960s as a means of measuring incoming international students’ English proficiency. But like any test, the PTE is imperfect. For instance, whereas a high PTE score may be sufficient to admit an international student to an American graduate school, many colleges and universities require more rigorous proof of a student’s English proficiency—often in the form of a passing score on a school-specific oral assessment—if he seeks employment as a Graduate Teaching Assistant (GTA). How to PTE Scores to Predict in SPEAK Test

To mitigate this risk, forecasting models which use the PTE sub-scores of Speaking, Listening, Writing, and Reading to forecast SPEAK test outcome are applied. A student’s sub-scores act as predictive inputs to each model, which outputs the posterior probability of his SPEAK test failure. Bayes Theorem provides the structure required to obtain this probability, and the multivariate meta-Gaussian distribution captures the stochastic dependence between the sub-scores. Therefore, these models are classified as Bayesian Meta-Gaussian Forecasters (BMGFs).

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TOEFL English test for the Graduate and School students

To correspond to drastic change in international society such as “globalization”, graduate education plays a key role in development of human resources. The new trans-graduate-school education program called “Nitobe School” was launched in 2015 as one of the main education projects of “Top Global University Project” in Hokkaido University. This is a trial case report of the comparison of commercial English test for the Graduate School students. We employed about 50 students from the various graduate schools in Hokkaido University, and they took same commercial English tests (TOEIC and TOEFL), then its result is discussed in here. TOEFL English test for the Graduate and School students

The pre-post English speaking test: Describing the picture , Pre-English Speaking Test was used without teaching. Post-English Speaking Test was used after teaching.

The English speaking ability evaluation form. The evaluation criteria were as follows: Grammar and vocabulary ,Structures: Process of speaking , Fluency and pronunciation , Self confidence,Persuasiveness .

The collected data was analyzed using computer program. The t-test was employed to compare the subjects’ English speaking ability before and after using English speaking based on communicative approach. The mean and standard deviations of scores from English speaking evaluation form, the satisfaction questionnaire were used to measure at the end of the class.

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How to Test of Spoken English Competence Advanced Learners

It is evident that English has become an essential part of everyone’s life. In higher education, especially graduate studies, English inevitably plays a crucial role in determining the success or failure of the students. In order to screen applicants for graduate studies, it is important to devise a standardized test which is reliable, valid, and practical. Conventionally, most English proficiency tests in Thailand will have three subtests in common. These sub tests include: Error identification, Multiple-choice cloze, and Reading comprehension. How to Test of Spoken English Competence Advanced Learners

Teaching speaking English has a crucial role in English instruction as a foreign language. That’s because teaching English based on communicative approach theory is worldwide. Therefore, Institute of Technology emphasizes communicative approach teaching. Thus the students need to have competence in listening and speaking. [9] stated that the principle of communicative training involves the use of complex communicative situations, aimed at developing the pupil’s speech that promotes “overcoming a sharp transition from education to the natural conditions of communication due to the formation of students’ strong associative links”. The teacher becomes free to choose a variety of instructional techniques and incentives that can maintain motivation and mental activity of students during the entire study period.

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Information System for Game TOEFL like App

Educative games are trending among students. Games can be used to support student learning.Difficulties in completing a TOEFL (Test of English as a Foreign Language) can be helped by doing game exercises that are similar to the actual test conditions. The TOEFL learning method while playing the game becomes an interesting project, to see how students can use the game experience to master the skills needed for doing a TOEFL. Information System for Game TOEFL like App

One of the criteria of an English student‟s mastery in the language is to show how proficient that student is in doing a TOEFL. For this reason, a TOEFL-Like App game has been created by the researchers, who are a team made up from the English Department and Information Systems Department that specifically deals with Game Technology. The researchers see that managing the results of a TOEFL test that goes on for almost an hour, needs to be assisted with some kind of system to ease the burden of the English teachers. Henceforth, the TOEFL-Like Game App is built with an information system to help teachers access the final results online.

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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

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

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

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

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

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 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

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

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

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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