Category Archive : Best Online Ielts Coaching

Programs in Non-Native English Speaking Nations

Programs in Non-Native English Speaking Nations

The essential element of globalization is communications in English. Technology and social science will influence the non-native English speaking nations (non-NESN) through the English communication. These kinds of communications are very important to these countries, because some information will make the non-NESN a great deal progress. In Taiwan, the globalizing policy has been proposed and stressing for a long time. Because Mandarin and some dialects are widely used in daily life, the general English level in Taiwan isn’t as good as that in some native English speaking nations (NESN). From the viewpoints of globalization, the English level in Taiwan should be elevated to approach the level of the NESN. The English teaching effectiveness directly affects the globalizing pace. The school-based curricula (SBC) in the technological and vocational education systems are developed by the Ministry of Education to cope with the rapid changes in the Age of Knowledge-based Economy [1-2]. These curricula are divided into 17 occupational families (OFs) are shown in Appendix [3-4]. This new SBC is going to work in practice in 2005. The 18 year-old students at the moment will work on the 2-year technological and vocational college/university (TVCU) program, and 16 year-old students will work on the 4-year TVCU program. Since they study English at the junior high school (about 12 years old), their English level could not be good enough to handle all globalizing things related to English after they graduate from 2-year or 4-year TVCU program. When they study in SBC, instructors can’t teach them in the same way as those in NESN. English is one of the core courses of the 17 OFs. It is needless to say that English plays the very important role in the Age of Knowledge-based Economy [5]. Therefore, English courses should be systematically designed. The purpose of this study is to systematically design SBC for the students in TVCU programs. After they graduate, they can cooperate with several OFs to communicate with the professionals in NESN smoothly, and speed up the globalizing pace. https://speakinenglish.in/

The essential element of globalization is communications in English. We proposed the spectrum analysis of English vocabulary for the professionals to communicate between NESN and non-NESN. The English curriculum overview of TVCU program is developed from the spectrum in Fig. 1. This method is helpful to the curriculum design of TVCU programs which will be put into practice in 2005. Due to the analysis and the curriculum design, the globalizing pace will be accelerated

https://www.facebook.com/ECS-IELTS-1177433205637186/
https://goo.gl/maps/o6qrh5hYFDBL9Uk96

GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

In this paper, we propose ground truth estimation of spo-ken English fluency scores using decorrelation penalized low-rank matrix factorization. Automatic spoken English fluencyscoring is a general classification problem. The model param-eters are trained to map input fluency features to correspond-ing ground truth scores, and then used to predict a score for aninput utterance. Therefore, in order to estimate the model pa-rameters to predict scores reliably, correct ground truth scoresmust be provided as target outputs. However, it is not simpleto determine correct ground truth scores from human raters’scores, as these include subjective biases. Therefore, groundtruth scores are usually estimated from human raters’ scores,and two of the most common methods are averaging and vot-ing.Although these methods are used successfully, ques-tions remain about whether the methods effectively estimateground truth scores by considering human raters’ subjectivebiases and performance metric. Therefore, to address theseissues, we propose an approach based on low-rank matrix fac-torization penalized by decorrelation. The proposed methoddecomposes human raters’ scores to biases andlatentscoresmaximizing Pearson’s correlation. The effectiveness of theproposed approach was evaluated using human ratings of theKorean-Spoken English Corpus.https://speakinenglish.in/

Recently, Computer Aided Language Learning (CALL) hasreceived considerable attention as a method for improving theEnglish speaking skills of non-native students. In order forCALL systems to provide useful tutoring feedback, an auto-mated scoring system is required for evaluating pronunciationquality, fluency, and specific mistakes made by non-nativestudents.In general, the fluency scoring system is composed of au-tomatic speech recognition, fluency feature extraction, anda scoring model. In fluency feature extraction, features as-sumed to be highly correlated to spoken English fluency arecomputed [1, 2, 3]. For example, long silence duration, num-ber of words per second, and phone duration are some of themost common fluency features [1, 4]. The scoring model is aclassifier in which the model parameters are trained to map in-put fluency features to corresponding ground truth scores, andthen used to predict a score for an input utterance. The mostcommon algorithms for scoring models are linear regression[2], support vector machine (SVM) [5], or Gaussian process[3].Score modelling is a general supervised learning problem.Therefore, in order for the model to be trained reliably, correctground truth scores must be provided as target outputs. How-ever, it is not simple to obtain correct ground truth scores fromhuman raters’ scores as these include variability due to humanraters’ subjective biases. For example, each human expertmight assign different scores to the same utterances. Con-sequently, ground truth scores are estimated by neutralizinghuman raters’ subjective biases. The most common methodis averaging, which estimates ground truth scores by averag-ing the biased scores [6, 7, 8, 9]; Another is voting, which isbased on majority opinions [10].Although averaging and voting are successfully usedin practice, questions remain about whether they producereliable ground truth scores by considering human raters’biases and scoring model metric such as Pearson’s correla-tion. Therefore, we propose an estimation approach based ondecorrelation penalized low-rank matrix factorization to takeaccount of both human raters’ subjective bias and Pearson’scorrelation.

GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

In spoken English fluency scoring modelling, the ground truthestimation problem is sometimes overlooked because a scor-ing rubrics are designed and human raters are trained to main-tain high correlation among their scores. Nevertheless, thereexists disagreement among raters’ scores and a single scoremust be determined for an each input feature to train the com-putational scoring model such as DNN.

https://www.facebook.com/ECS-IELTS-1177433205637186/
https://goo.gl/maps/o6qrh5hYFDBL9Uk96