Sechun Kang’s research while affiliated with Pohang University of Science and Technology and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (4)


A sample of sentence stress feedback
Architecture of the proposed system
Correlations between the learners' proficiency levels and the feedback scores determined by θ1 and θ2; (a) for an overview, (b) for θ1 and (c) for θ2
Distribution of feedback scores over proficiency levels
A sample from the KLEAC
Automatic sentence stress feedback for non-native English learners
  • Article

June 2016

·

191 Reads

·

22 Citations

Computer Speech & Language

·

Ho-Young Lee

·

·

[...]

·

Hyosung Hwang

This paper proposes a sentence stress feedback system in which sentence stress prediction, detection, and feedback provision models are combined. This system provides non-native learners with feedback on sentence stress errors so that they can improve their English rhythm and fluency in a self-study setting. The sentence stress feedback system was devised to predict and detect the sentence stress of any practice sentence. The accuracy of the prediction and detection models was 96.6% and 84.1%, respectively. The stress feedback provision model offers positive or negative stress feedback for each spoken word by comparing the probability of the predicted stress pattern with that of the detected stress pattern. In an experiment that evaluated the educational effect of the proposed system incorporated in our CALL system, significant improvements in accentedness and rhythm were seen with the students who trained with our system but not with those in the control group.



An automatic pitch accent feedback system for english learners with adaptation of an english corpus spoken by Koreans

December 2012

·

22 Reads

·

1 Citation

To improve the English proficiency of Korean learners, we design a system for pitch accents, which consists of prediction, detection and feedback parts. The prediction and detection parts adopt Conditional Random Field models to achieve a prediction accuracy of 87.25%, which is based on the Boston University radio news corpus, and a detection accuracy of 81.21%, which is based on the Korean Learner's English Accentuation corpus. In the learner experiment with our system, learners' pitch accent proficiency, as assessed by English experts, was improved from 2.67 to 3.25 on a scale of 1-to-5, and the accuracy of not-wrong feedback was measured at 82.77%. The learners assessed the learning effectiveness of our system at 4.3 on a scale of 1-to-5.


A meta learning approach to grammatical error correction
  • Conference Paper
  • Full-text available

July 2012

·

57 Reads

·

6 Citations

We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors. A series of experiments is presented to show the effectiveness of the proposed approach on two different grammatical error tagged corpora.

Download

Citations (4)


... 1 http://www.coelang.tufs.ac.jp/mt/fr-swiss/dmod/index_en.html 2 http://www.enterate.unam.mx/Articulos/2007/abril/sarahi.html 3 http://www.carnegiespeech.com/products/nativeaccent.php Among those that do, some have developed procedures for automatic assessment of non-native prosody (e.g., [10] for English L2; [11], [12] for Spanish L2), while others have examined the impact of feedback in learning L2 prosody (e.g., [13], [14] for German L2; [15] for English L2). Although some existing systems were specifically built for the automatic detection of stress errors (e.g., [16] for English L2; [17] for German L2, [18] for Hungarian L2), and hence designed to improve the learners' production of stress in L2, none to our knowledge have focused on learners' perception of stress in a second language. ...

Reference:

MIAPARLE: Online training for discrimination and production of stress contrasts
An automatic feedback system for English speaking integrating pronunciation and prosody assessments
  • Citing Conference Paper
  • August 2013

... If a person wants to be successful in the process of learning a new language, one of the most important qualities they can possess is the capacity for clear and concise communication. Therefore, learning a second language is a way that one can increase their ability to communicate with others (Lee et al., 2017). Because of this, there is a greater requirement to practice helpful skills, such as giving presentations in public settings. ...

Automatic sentence stress feedback for non-native English learners
  • Citing Article
  • June 2016

Computer Speech & Language

... 2 Related Work 2.1 Meta Learning Meta learning, a.k.a "learning to learn", intends to design models that can learn general data representation and adapt to new tasks with a few training samples (Finn et al., 2017;Nichol et al., 2018). Early works have demonstrated that meta learning is capable of boosting the performance of natural language processing (NLP) tasks, such as named entity recognition (Munro et al., 2003) and grammatical error correction (Seo et al., 2012). ...

A meta learning approach to grammatical error correction

... In Kang et al. (2012), we used the BURNC (Boston University Radio News Corpus), where pitch accents are labeled according to the ToBI system (Silverman et al., 1992) to produce a pitch accent prediction model. The accuracy of sentence stress prediction with this model was only 83.7%, and thus not satisfactory enough to be used for teaching stress-timed English rhythm to non-native learners because pitch accent is imposed on some, but not all, stressed words in a sentence. ...

An automatic pitch accent feedback system for english learners with adaptation of an english corpus spoken by Koreans
  • Citing Conference Paper
  • December 2012