• Speech Sciences
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• v.10 no.4
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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.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.41-44
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• 2002

This paper is related to the enhancement of speech recognition rate using enhanced pronunciation dictionary. Modern large vocabulary, continuous speech recognition systems have pronunciation dictionaries. A pronunciation dictionary provides pronunciation information for each word in the vocabulary in phonemic units, which are modeled in detail by the acoustic models. But in most speech recognition system based on Hidden Markov Model, actual pronunciation variations are disregarded. Without the pronunciation variations in the speech recognition system, the phonetic transcriptions in the dictionary do not match the actual occurrences in the database. In this paper, we proposed the unvoiced rule of semivowel in allophone rules to pronunciation dictionary. Experimental results on speech recognition system give higher performance than existing pronunciation dictionaries.

• MALSORI
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• no.65
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• pp.93-103
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• 2008

In this paper, we propose a method for optimizing a multiple pronunciation dictionary used for modeling pronunciation variations of non-native speech. The proposed method removes some confusable pronunciation variants in the dictionary, resulting in a reduced dictionary size and less decoding time for automatic speech recognition (ASR). To this end, a confusability measure is first defined based on the Levenshtein distance between two different pronunciation variants. Then, the number of phonemes for each pronunciation variant is incorporated into the confusability measure to compensate for ASR errors due to words of a shorter length. We investigate the effect of the proposed method on ASR performance, where Korean is selected as the target language and Korean utterances spoken by Chinese native speakers are considered as non-native speech. It is shown from the experiments that an ASR system using the multiple pronunciation dictionary optimized by the proposed method can provide a relative average word error rate reduction of 6.25%, with 11.67% less ASR decoding time, as compared with that using a multiple pronunciation dictionary without the optimization.

• Annual Conference on Human and Language Technology
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• 2005.10a
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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.

• Cross-Cultural Studies
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• v.38
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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.

• Proceedings of the KSPS conference
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• 2003.05a
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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.

• MALSORI
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• no.48
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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.

• Speech Sciences
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• v.8 no.1
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• pp.33-52
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• 2001

A bilingual sound dictionary in Korean and English has been created for a broad range of sound reference to cross-linguistic, dialectal, native language (L1)-transferred biological and allophonic variations. The paper demonstrates that the pronunciation dictionary of the lexicon is inadequate for sound reference due to the preponderance of unmarked sounds. The audio registry consists of the three-way comparison of 1) English speech from native English speakers, 2) Korean speech from Korean speakers, and 3) English speech from Korean speakers. Several sub-dictionaries have been created as the foundation research for independent development. They are 1) a pronunciation dictionary of the Korean lexicon in a keyboard-compatible phonetic transcription, 2) a sound dictionary of L1-interfered language, and 3) an audible dictionary of Korean sounds. The dictionary was designed to facilitate the exchange of the speech signal and its corresponding text data on various media particularly on CD-ROM. The methodology and findings of the construction are discussed.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.151-162
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• 2009

This paper aims to investigate how tensification preference has changed over time and discuss how appropriately tensification preference is reflected in Principles of Standard Pronunciation and Standard Korean Language Dictionary. For this research, a questionnaire survey of tensification preference was conducted. 173 test words were used and 156 native Seoul speakers participated in this survey. The results have shown that tensification preference has gradually increased from older to younger generations. In addition, Principles of Standard Pronunciation and Standard Korean Language Dictionary do not reflect real pronunciation appropriately. Therefore, some ways of incorporating the actual pronunciation of Seoul speakers in the Principles of Standard Pronunciation and the Standard Korean Language Dictionary are suggested.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.21-27
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• 1999

In this paper, we propose an improved MLP-based English pronunciation dictionary generator to apply to the variable vocabulary word recognizer. The variable vocabulary word recognizer can process any words specified in Korean word lexicon dynamically determined according to the current recognition task. To extend the ability of the system to task for English words, it is necessary to build a pronunciation dictionary generator to be able to process words not included in a predefined lexicon, such as proper nouns. In order to build the English pronunciation dictionary generator, we use context-dependent grapheme-to-phoneme multi-layer perceptron(MLP) architecture for each grapheme. To train each MLP, it is necessary to obtain grapheme-to-phoneme training data from general pronunciation dictionary. To automate the process, we use dynamic programming(DP) algorithm with some distance metrics. For training and testing the grapheme-to-phoneme MLPs, we use general English pronunciation dictionary with about 110 thousand words. With 26 MLPs each having 30 to 50 hidden nodes and the exception grapheme lexicon, we obtained the word accuracy of 72.8% for the 110 thousand words superior to rule-based method showing the word accuracy of 24.0%.