• 대한음성언어의학회:학술대회논문집
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• 대한음성언어의학회 2003년도 제19회 학술대회
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• pp.170-173
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• 2003

There are more than five systems for transcribing English vowels. Because of this diversity, teachers of English and students are confronted with not a little problems with the English vowel symbols used in the English-Korean dictionaries, English text books, books for Phonetics and Phonology. This study was designed to suggest criterions for the phonemic transcription of English vowels on the basis of phonetic properties of the vowels and a system of English vowel transcription based on the criterions in order to minimize the problems with inter-system differences. A speaker (phonetician) of RP English uttered a series of isolated minimal pairs containing the vowels in question. The suggested vowel symbols are as follows: 1) Simple vowels : /i:/ in beat, /I/ bit, /$\varepsilon$/ bet,/${\ae}$/ bat, /a:/ father, /Dlla/ bod, /$\jmath$:/ bawd, /u/ put, /u:/ boot /$\Lambda$/ but, and /$\partial$/ about /$\Im$:ll$\Im$:r/ bird. 2) Diphthongs : /aI/ in bite, /au/ bout, /$\jmath$I/ boy, /$\Im$ullou/ boat, /er/ bait, /e$\partial$lle$\partial$r/ air, /u$\partial$llu$\partial$r/ poor, /i$\partial$lli$\partial$r/ beer. Where two symbols are shown corresponding to the vowel in a single word, the first is appropriate for most speakers of British English and the second for most speakers of American English.

• 정보처리학회논문지B
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• 제11B권1호
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• pp.99-106
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• 2004

음절 복원은 음성 인식 장치에서 인식된 음가열을 발성 이전의 표기 형태로 복원하는 과정이다. 본 논문에서는 음절 복원 과정을 위하여 표준 발음법을 기반으로 음절 복원 규칙을 작성하였다. 음절 복원 규칙을 이용하여 표기 후보 집합의 생성 방법을 연구하였다. 또한 생성된 표기후보의 수를 감소시키기 위하여, 비 표기 음절을 포함한 표기 후보 감소, 비 어휘 음절을 포함한 표기 후보 감소, 비어간 음절을 포함한 표기 후보 감소의 3단계 감소 과정을 제안하였다. 제안된 방법을 통하여 실험한 결과 평균 74%의 표기 후보 감소율을 나타내었다.

• 복식
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• 제57권6호
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• pp.112-121
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• 2007

Gamtu was a name recorded in Korean in respect of 'Mo(帽), Moja(帽子), Somoja(小帽子), Jeongmoja(頂帽子), Jeonmo(氈帽), Yangmomo(羊毛帽), Tangjeon(宕巾)' from the beginning to the end of Joseon dynasty which includes various shapes and uses according to wordbook. Also, Gamtu has various phonetic transcriptions, however Manchu pronunciation of 'Moja(帽子)' is indicated as 'Kamtoo' in $\ulcorner$Dongmunyuhae(同文類解)$\lrcorner$ so the origin of the word Gamtu should be discussed. Accordingly, I want to clearly define the concept of Gamtu in this research by studying the history and shape of Gamtu, and Chinese entries annotated in Korean as Gamtu. Even though we can assume that the etymology of Gamtu is Manchu language as Manchu pronunciation of 'Moja(帽子)' is indicated as 'Kamtu' in $\ulcorner$Dongmunyuhae(同文類解)$\lrcorner$, I think the Korean pronunciation of Gamtu already existed because there already was a Chinese transcription of 'Gamtu' in the Goryeosa(高麗史). So, we can conjecture that Gamtu could be originated from Yeojin language which is the same lineage of Manchu. As a conclusion, Somoja(小帽子), Jeongmoja(頂帽子), Moja(帽子), Jeonmo(氈帽), Yangmomo(羊毛帽), Tangjeon(宕巾) had a common feature that they were produced to fit head without visor, and were annotated as Gamtu per woolen cloth was used as Jeonmo(氈帽), Yangmomo(羊毛帽), and Gamtu was just a Korean transcription that includes these names.

• ETRI Journal
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• 제46권1호
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• pp.127-136
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• 2024

The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule-based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data-driven Korean spoken-to-written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.

• 한국콘텐츠학회논문지
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• 제14권2호
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• pp.28-38
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• 2014

서양 음악의 경우 자동채보와 내용기반 음악검색을 위한 음악 정보 분석연구가 활발하게 진행되고 있다. 그러나 한국 전통음악에서는 유사한 연구사례를 찾아보기 어렵다. 본 논문에서는 한국의 전통음악인 판소리 구조를 자동으로 분석하기 위한 알고리즘들을 제안한다. 제안된 알고리즘은 음성과 비음성의 시간 간격비율을 이용하여 '소리' 부분과 '아니리' 부분을 자동으로 구분한다. 뿐만 아니라 알고리즘은 '장단'이라 칭하는 리듬을 템플릿 이용한 다수결 결정 방법으로 강건하게 구분한다. 또한 알고리즘은 칼만 필터를 이용하여 '소리' 부분의 마디 지점을 검지해낸다. 본 논문에서 제안된 알고리즘들은 판소리 샘플들에서 양호하게 동작하였으며 자동채보의 전단계의 구조분석에 유용할 수 있다.

• 대한음성학회지:말소리
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• 제51호
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• pp.71-84
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• 2004

In this paper, we propose the automatic speech database verification method(or called automatic verification) based on confidence measure for a large speech database. This method verifies the consistency between given transcription and speech using the confidence measure. The automatic verification process consists of two stages : the word-level likelihood computation stage and multi-level likelihood ratio computation stage. In the word-level likelihood computation stage, we calculate the word-level likelihood using the viterbi decoding algorithm and make the segment information. In the multi-level likelihood ratio computation stage, we calculate the word-level and the phone-level likelihood ratio based on confidence measure with anti-phone model. By automatic verification, we have achieved about 61% error reduction. And also we can reduce the verification time from 1 month in manual to 1-2 days in automatic.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2006년도 추계학술대회 발표논문집
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• pp.135-138
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• 2006

In this paper, we introduce a method for extracting grapheme-to-phoneme conversion rules from the transcription of speech synthesis database and a prosody modeling method using the light version of ToBI for a Korean conversational style TTS. We focused on representing the characteristics of the conversational speech style and the experimental results show that our proposed methods are suitable for developing a Korean conversional style TTS.

• 음성과학
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• 제8권1호
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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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (2)
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• pp.401-403
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• 2000

본 논문에서는 일한 음차 변환을 이용한 음성인식 및 합성기를 구현하였다. 음성인식의 경우 CV, VCCV, VCV, VV, VC 단위를 사용하였다. 이와 같이 단위별로 미리 구축된 모델을 결합함으로써 음성인식 시스템을 구축하였다. 따라서 일한 음차 변환을 적용하게 되면 인식 대상이 일어단어일 경우에도 이를 한글 발음으로 변환한 후 그에 해당하는 모델을 생성함으로써 인식이 가능하다. 음성 합성기의 경우 합성에 필요한 한국어 음성 데이터 베이스를 구축하고, 입력되는 텍스트에 따라 이를 연결하여 합성음을 생성한다. 일어가 입력될 경우 일한 음차 변환 규칙을 이용하여 입력된 일어 발음을 한글로 바꾸어 준 후 입력하게 되므로 별도의 일어 합성기 없이도 합성음을 생성할 수 있다.

• 대한음성학회지:말소리
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• 제61호
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• pp.101-111
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• 2007

A modified speech recogniser have been proposed for automatic capitalisation generation to improve the readability of English speech recognition output. In this modified speech recogniser, every word in its vocabulary is duplicated: once in a de-caplitalised form and again in the capitalised forms. In addition its language model is re-trained on mixed case texts. In order to evaluate the performance of the proposed system, experiments of automatic capitalisation generation were performed for 3 hours of Broadcast News(BN) test data using the modified HTK BN transcription system. The proposed system produced an F-measure of 0.7317 for automatic capitalisation generation with an SER of 48.55, a precision of 0.7736 and a recall of 0.6942.