• Korean Journal of English Language and Linguistics
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• v.2 no.1
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• pp.123-147
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• 2002

As far as the recent ELT research concerned, it seems to have been no hot ‘theoretical’ issues, but ‘practical’ ones in general: e.g., learners and learning, components of proficiency, correlates of L2 learning, etc. This paper focuses on the theme given above, with a special reference to the sub-title: specifically, 1) World English, world Englishes and world's lingua franca; 2) ENL, ESL and EFL; 3) Grammars, style manuals, dictionaries and media; 4) Pronunciation models: RP, BBC model and General American, Network Standard; 5) Lexical, grammatical variations and discourse grammars; 6) Beliefs and subjective theories in foreign language research; 7) Dilemma among radical, canonical and eclectic views. In conclusion, the author offers a modest proposal: we need to appeal to our own experience, intention, feeling and purpose, that is, our identity to express “our own selves” in our contexts toward the world anywhere, if not sounding authentic enough, but producing it plausibly well. It is time for us (with our ethno-cultural autonomy) to need to be complementary to and parallel with its native speakers' linguistic-cultural authenticity in terms of the broadest mutual understanding.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.925-927
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• 2004

음차표기란 외국어의 발음을 자국어로 표기하는 것으로 정의된다. 영-한 자동 음차표기 방법에는 직접방식, 피봇방식, 혼합방식이 있다. 기존의 영-한 음차표기 연구들은 직접방식에 기반한 연구들이 대부분이었다. 하지만, 음차표기는 직접방식에서 사용하는 단순한 자소 대 자소변환 작업이라기보다는 자소의 음성적 변환 작업이라고 할 수 있다. 따라서 자소뿐만 아니라 음소 등 음성적 정보가 매우 중요하다. 본 논문에서는 이러한 특성을 이용하여 자소 정보뿐만 아니라 음소 정보를 이용한 음차표기 기법을 제안한다. 주어진 자소와 음소 및 자소와 음소의 문맥정보를 이용하여 한국어 음차표기를 생성하는 본 논문의 기법은 약 60%의 단어정확도를 나타내었다.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.601-606
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• 2019

This paper is a study on speech recognition in the Korean using grapheme unit (Cho-sumg [onset], Jung-sung [nucleus], Jong-sung [coda]). Here we make ASR (Automatic speech recognition) system without G2P (Grapheme to Phoneme) process and show that Deep learning based ASR systems can learn Korean pronunciation rules without G2P process. The proposed model is shown to reduce the word error rate in the presence of sufficient training data.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.483-489
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• 2020

This paper proposes acoustic model training using self-attention for low-resource speech recognition. In low-resource speech recognition, it is difficult for acoustic model to distinguish certain phones. For example, plosive /d/ and /t/, plosive /g/ and /k/ and affricate /z/ and /ch/. In acoustic model training, the self-attention generates attention weights from the deep neural network model. In this study, these weights handle the similar pronunciation error for low-resource speech recognition. When the proposed method was applied to Time Delay Neural Network-Output gate Projected Gated Recurrent Unit (TNDD-OPGRU)-based acoustic model, the proposed model showed a 5.98 % word error rate. It shows absolute improvement of 0.74 % compared with TDNN-OPGRU model.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.23-32
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• 2006

This paper describes the optimization of a language model and an acoustic model to improve speech recognition using Korean unit digits. Since the model is composed of a finite state network (FSN) with a disyllable, recognition errors of the language model were reduced by analyzing the grammatical features of Korean unit digits. Acoustic models utilize a demisyllable pair to decrease recognition errors caused by inaccurate division of a phone or monosyllable due to short pronunciation time and articulation. We have used the K-means clustering algorithm with the transformed successive state splitting in the feature level for the efficient modelling of feature of the recognition unit. As a result of experiments, 10.5% recognition rate is raised in the case of the proposed language model. The demi-syllable fair with an acoustic model increased 12.5% recognition rate and 1.5% recognition rate is improved in transformed successive state splitting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.13-20
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• 2016

This study explores the effects of an video-recording English speaking task model on learners. The learning model, a form of mobile learning, was developed to facilitate the learners' output practice applying advantages of a smartphone and Text-to Speech. The survey results shows the positive effects of the speaking task on the domain of pronunciation, speaking, listening, writing in terms of students' confidence, as well as general English ability. The study further examines the possibilities and limitations of the speaking task in assisting Korean learners improve their speaking ability, who do not have sufficient exposure to English input or output practice due to the situational limitations where English is learned as a foreign language.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.299-304
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• 2016

The importance of clear and effective communication at sea has been greatly emphasized due to the increase in multiculturalism on board both ocean-going and coastal vessels, and the necessity of systematic English training based on 'Knowledge, Understanding, and Proficiency' specified in STCW has also been recognized. With these growing needs in mind, the International Maritime Organization (IMO) updated the Maritime English (ME) Model Course 3.17 in 2015 by providing guidelines on language education within two separate categories, General Maritime English (GME) and Specialized Maritime English (SME). The IMO is now attempting to create a new, global framework of ME education and training, and this this new course model must first be thoroughly understood in order to explore the ways to apply the modified version into the context of current ME education in Korea and to design an updated language curriculum. Therefore, the general structural features of the new model course will be explained in this paper, and the course focus set by IMO and to be considered and/or adopted by the Republic of Korea will be closely examined. Finally, suggestions will be made on how to implement this revised model course in practice with the following focus: the development of localized curriculum for GME and SME; the provision of practical teaching guidance through relevant online and offline materials for class and self-study; and the establishment of qualification guidelines and a teaching support system for language teachers in maritime and language education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1625-1631
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• 2005

This paper describes an optimization of a language model and an acoustic model that improve the ability of speech recognition with Korean nit digit. Recognition errors of the language model are decreasing by analysis of the grammatical feature of korean unit digits, and then is made up of fsn-node with a disyllable. Acoustic model make use of demi-syllable pair to decrease recognition errors by inaccuracy division of a phone, a syllable because of a monosyllable, a short pronunciation and an articulation. we have used the k-means clustering algorithm with the transformed successive state splining in feature level for the efficient modelling of the feature of recognition unit . As a result of experimentations, $10.5\%$ recognition rate is raised in the case of the proposed language model. The demi-syllable pair with an acoustic model increased $12.5\%$ recognition rate and $1.5\%$ recognition rate is improved in transformed successive state splitting.

• Culinary science and hospitality research
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• v.21 no.4
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• pp.251-270
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• 2015

This study was, based on traditional know-how indigenous to Korea, to systemize the knowledge on how to improve health by dining, and to make the best of it statistically. For this purpose, the knowledge in the Sikryo-chanryo(食療纂要), in Korean pronunciation and Siglyochan-yo in Chinese characters, which is an old text referring to diet therapy peculiar to Korea, was compiled into a database and analyzed statistically. Data processing was used as a 'Relational data model'. In addition, we have used nine data table to express diet therapy peculiar to Korea in the Siglyochan-yo. The software used for data construction was Microsoft Access 2014. As a result, the Sikryo-chanryo database, which can provide information on both disease treatment by food, medicines, and gourmet ingredients applicable to every kind of symptom, as well as the names of disease, was set up at in a PC interface. By employing the 'Relational data model', we can replace researching in the conventional method by employing the database.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.1-7
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• 2021

Recently, since most of the research on correcting speech recognition errors is based on English, there is not enough research on Korean speech recognition. Compared to English speech recognition, however, Korean speech recognition has many errors due to the linguistic characteristics of Korean language, such as Korean Fortis and Korean Liaison, thus research on Korean speech recognition is needed. Furthermore, earlier works primarily focused on editorial distance algorithms and syllable restoration rules, making it difficult to correct the error types of Korean Fortis and Korean Liaison. In this paper, we propose a context-sensitive post-processing model of speech recognition using a LSTM-based sequence-to-sequence model and Bahdanau attention mechanism to correct Korean speech recognition errors caused by the pronunciation. Experiments showed that by using the model, the speech recognition performance was improved from 64% to 77% for Fortis, 74% to 90% for Liaison, and from 69% to 84% for average recognition than before. Based on the results, it seems possible to apply the proposed model to real-world applications based on speech recognition.