• 한국어교육
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• 제28권3호
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• pp.105-126
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• 2017

Intelligibility has been widely regarded as an appropriate goal for second language pronunciation teaching. Yet there are few empirical studies that focus on the intelligibility of Korean learners' pronunciation. Therefore, this mixed-methods study examined the relationship among native-like pronunciation, intelligible pronunciation, phonological fluency and comprehensibility. Furthermore, this study investigated how native-like pronunciation and intelligible pronunciation are measured differently in terms of actual pronunciation skills. In addition, this study examined how these two pronunciation styles mutually influence each other. The results of this study show that achieving native-like pronunciation is a much more difficult goal than achieving intelligible pronunciation. It further shows that foreign accented pronunciation has little to do with comprehensibility while better intelligibility is needed for clearer comprehensibility. To achieve better intelligibility, this study recommends pronunciation teaching based on segments, suprasegmentals and fluency as the focus on suprasegmentals and fluency teaching are more important to achieve a native-like pronunciation. Besides the focus on phonology, there are other social factors which could influence the evaluation of native-like pronunciation, but are not part of this study. These findings are expected to contribute not only to a better understanding of pronunciation, but also to a more comprehensive reevaluation and informed direction of pronunciation teaching and research.

• 비교문화연구
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• 제38권
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• pp.405-431
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• 2015

The purpose of this paper is to investigate how to decide pronunciation of loanwords in Korean language. There has not been a regulation for pronunciation of loanwords in Korean language. Even the dictionary published by the government does not provide any information about the pronunciation of loanwords. In this paper, some actual solutions are suggested for the pronunciation of loanwords. Korean language has Regulations of Standard Korean, Korean Orthography, Regulations on Hangeul Transcriptions on Loanwords and Pronunciation Methods of Standard Korean. These language standards could help to decide pronunciation of loanwords. Some pronunciations which could not be regulated by them must be presented in the standard pronunciation dictionary. For example, glottalization rule of 's' in many loanwords could be presented in the description of each loanword in the dictionary. However the pronunciation of loanwords must be similar to the spelling. If various pronunciations are allowed to one spelling, then people will be so confused by the discrepancy between pronunciation and spelling of loanwords.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.168-171
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• 2003

This study is about modeling pronunciation dictionary necessary for PLU(phoneme like unit) level word recognition. The recognition of nonnative speakers' pronunciation enables an automatic diagnosis and an error detection which are the core of English pronunciation tutoring system. The above system needs two pronunciation dictionaries. One is for representing standard English pronunciation. The other is for representing Korean speakers' English Pronunciation. Both dictionaries are integrated to generate pronunciation networks for variants.

• 대한음성학회지:말소리
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• 제55권
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• pp.103-118
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• 2005

In this paper, we describe a morpheme-based pronunciation lexicon useful for Korean LVCSR. The phonemic-context-dependent multiple pronunciation lexicon improves the recognition accuracy when cross-morpheme pronunciation variations are distinguished from within-morpheme pronunciation variations. Since adding all possible pronunciation variants to the lexicon increases the lexicon size and confusability between lexical entries, we have developed a lexicon pruning scheme for optimal selection of pronunciation variants to improve the performance of Korean LVCSR. By building a proposed pronunciation lexicon, an absolute reduction of $0.56\%$ in WER from the baseline performance of $27.39\%$ WER is achieved by cross-morpheme pronunciation variations model with a phonemic-context-dependent multiple pronunciation lexicon. On the best performance, an additional reduction of the lexicon size by $5.36\%$ is achieved from the same lexical entries.

• 음성과학
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• 제10권4호
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• pp.167-177
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• 2003

This paper presents a method of building an exceptional pronunciation dictionary for Korean automatic pronunciation generator. An automatic pronunciation generator is an essential element of speech recognition system and a TTS (Text-To-Speech) system. It is composed of a part of regular rules and an exceptional pronunciation dictionary. The exceptional pronunciation dictionary is created by extracting the words which have exceptional pronunciations from text corpus based on the characteristics of the words of exceptional pronunciation through phonological research and text analysis. Thus, the method contributes to improve performance of Korean automatic pronunciation generator as well as the performance of speech recognition system and TTS system.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.9-16
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• 2019

Computer-Assisted Pronunciation Training system is considered to be a useful tool for pronunciation learning for students who received elementary level English pronunciation education, especially for students who have difficulty in correcting their pronunciation in front of others or who are not able to receive face-to-face training. The conventional Computer-Assisted Pronunciation Training system shows the word to the user, the user pronounces the word, and then the system provides phoneme or audio feedback according to the pronunciation of the user. In this paper, we propose a Computer-Assisted Pronunciation Training system that can practice on the varying pronunciation according to positions of adjacent phonemes. To achieve this, the proposed system is implemented by recommending a series of words by focusing on adjacent phonemes for simplicity and clarity. Experimental results showed that word recommendation considering adjacent phonemes leads to improvement of pronunciation accuracy.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2005년도 제17회 한글 및 한국어 정보처리 학술대회
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• pp.139-144
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• 2005

When a Japanese learner in Korea tries to find Japanese dictionary, he must know the pronunciation of the target word. But it's not easy to know the pronunciation of target word from Japanese sentence. Because most of general Japanese sentence shows only HanJa(Chinese character) instead of Kana(Japanese alphabet). If the Japanese learner knows the Korean traditional pronunciation of the target word, he can input the word to electronic Japanese dictionary with the Korean pronunciation. For this solution, the dictionary service provider must convert the Japanese word to Korean pronunciation, in advance. After setting of the conversions as a additional searching process, we can find the target word through Korean pronunciation of the Japanese HanJa, This process is possible for the three reasons below, 1. Korean, Japanese and Chinese are using the nearly same HanJa. The difference is small. 2. Most Japanese learner in Korea, knows the Korean pronunciation of the HanJa. 3. The Korean pronunciation of the HanJa is nearly unique, a HanJa has a Korean pronunciation, generally.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2000년도 7월 학술대회지
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• pp.255-270
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• 2000

Because the accuracy is essential, in order to get the fluency in speaking, both of them are very important in English education and in-service training programs. To get the accuracy and the fluency, the causes and phenomena of the unnatural pronunciation have to be surveyed first of all. Therefore, this article surveyed the problematic and unnatural pronunciation of Korean English teachers in elementary and secondary schools using CSL and Multi-speech. And also, tried to pinpoint what the causes of unnatural pronunciation are\ulcorner Next a procedure or steps were offered for them to speak naturally through in-service training programs. Through this analysis, it was found that elementary teachers have unnatural pronunciation below, within and beyond word level, and the secondary teacher has unnatural pronunciation within and beyond word level. Therefore, pronunciation training courses have to put emphasis on segment features first, and move to suprasegmental features for elementary teachers. For secondary teachers, pronunciation training courses have to focus on word level and move to suprasegmental features, in other words beyond word level. And these pronunciation training courses have to be run integrated.

• 대한음성학회지:말소리
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• 제48호
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• pp.57-67
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• 2003

This paper presents a systematic description of exceptional pronunciations for automatic Korean pronunciation generation. An automatic pronunciation generator in Korean is an essential part of a Korean speech recognition system and a TTS (Text-To-Speech) system. It is composed of a set of regular rules and an exceptional pronunciation dictionary. The exceptional pronunciation dictionary is created by extracting the words that have exceptional pronunciations, based on the characteristics of the words of exceptional pronunciation through phonological research and the systematic analysis of the entries of Korean dictionaries. Thus, the method contributes to improve performance of automatic pronunciation generator in Korean as well as the performance of speech recognition system and TTS system in Korean.

• 대한음성학회지:말소리
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• 제49호
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• pp.107-121
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• 2004

In this paper, we describe a cross-morpheme pronunciation variation model which is especially useful for constructing morpheme-based pronunciation lexicon to improve the performance of a Korean LVCSR. There are a lot of pronunciation variations occurring at morpheme boundaries in continuous speech. Since phonemic context together with morphological category and morpheme boundary information affect Korean pronunciation variations, we have distinguished phonological rules that can be applied to phonemes in within-morpheme and cross-morpheme. The results of 33K-morpheme Korean CSR experiments show that an absolute reduction of 1.45% in WER from the baseline performance of 18.42% WER was achieved by modeling proposed pronunciation variations with a possible multiple context-dependent pronunciation lexicon.