• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.469-471
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• 2004

기존의 전화기 자판은 여러 번의 키 누름에 의해 입력해야 하는 숨겨진 자음과 모음의 배열 때문에 문자 입력에 많은 불편이 따른다. 본 논문에서는 보다 쉬운 한글 입력을 위해서 휴대용 전화기의 키패드(Keypad)에 대한 분산형 한글 자판 배열과 그 자판 배열을 이용해서 쉽게 한글 문자를 입력할 수 있는 사용자 중심의 대화형 방법을 제시한다. 모든 단자음과 단모음이 키에 나타나 보이도록 중복 할당시키고 입력하고자 하는 글자를 구성하는 자소를 필기순서에 따라 해당키를 한번씩만 눌러서 입력할 수 있도록 했다. 또한 복자음이나 복모음의 입력을 글자의 필기 순서와 동일하게 입력하도록 하여 사용자의 문자 입력에 대한 부담이 최소화 되도록 했다. 모의실험을 통하여 기존의 방식보다 입력에 필요한 키 동작의 횟수가 줄었음을 확인했다.

• The Journal of the Acoustical Society of Korea
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• v.12 no.4
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• pp.57-62
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• 1993

본 논문은 신경망을 이용한 우리말 모음에 대한 인식 연구이다. 음성을 나누거나. 음소별 인식이나, 시간 신축 방법을 사용하지 않고 모음을 인식하였다. 식나의 변화에 따른 음성의 변화를 정적인 음성으로 취급하였다. 10개로 균등히 나눈 프레임에 각 프레임마다 10차의 PARCOR계수를 추출하였다. 신경망의 구조를 간단히 하기 위해서 단모음과 복모음을 구분하여 학습시켰으며, 출력 노드의 수를 감소시키기 위해 이진 코드 형태로 구성하였다.

• Cross-Cultural Studies
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• v.30
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• pp.259-277
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• 2013

This article covers realization of tone in Modern Chinese by the leverage principle, and then explores its teaching strategies. The results of this study are as follows: The teaching strategies are as follows: Firstly, pronouncing Chinese vowels always takes far longer than you anticipate. Secondly, pronounce and practice Chinese vowels with leverage principle. Thirdly, understand and practice the sound change rule of '?'.

• MALSORI
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• no.19_20
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• pp.33-42
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• 1990

Those who are interested in the German diphthongs wil1 find that they are classified into three kinds of forms in accordance with their gliding directions: closing, centring and rising. The German [aI], for example, which derives its origin from [i:] of the riddle high German. Is regarded as a distinctive feature that distinguishes the new high German from the middle high German. The diphthong [aI] is cal led fall ing one, because the sonority of the sound undergoes a diminution as the articulation proceeds. The end part of the diphthong [aI] is less sonorous than the beginning part. In most of the German diphthongs the diminution of prominence is caused by the fact that the end part is inherently less sonorous than the beginning. This applies to the other c los Ing and centring diphthongs. This way of diminution of sonority exerts influence on methods of constructing systems of phonetic notation. The above mentioned less sonorous end part of diphthong [I] shows that it differs from some analogous sound in another context. It is useful to demonstrate the occurrence of particular allophones by introducing special symbols to denote them (here: at→ae). Forms of transcription embodying extra symbol s are cal led narrow. But since strict adherence to the principle 'one sound one symbol' would involve the introduction of a large number of symbols, this would render phonetic transcriptions cumbrous and difficult to read. A broad style of transcription provides 'one symbol for each phoneme' of the language that is transcribed. Phonemic transcriptions are simple and unambiguous to everyone who knows the principles governing the use of allophones in the language transcribed. Among those German ways of transcriptions of diphthongs ( a?, a?, ??: ae, ao, ?ø; ae, ao, ?ø) the phonemic (broad) transcription is general Iy to be recommended, for Instance, in teaching the pronunciation of a foreign language, since it combines accuracy with the greatest measure of simplicity (Some passages and terms from Daniel Jones) .

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.99-106
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• 2006

Type classification is a very needed step in recognizing huge character set language such as korean characters. Since most previous researches are based on the composition rule of Korean characters, it has been difficult to correctly classify composite vowel characters and problem space was not divided equally for the lack of classification of last consonant which is relatively bigger than other graphemes. In this paper, I Propose a new type classification method in which horizontal vowel is extracted before vortical vowel and last consonants are further classified into one of five small groups based on horizontal projection profile. The new method uses 19 character types which is more stable than previous 6 types or 15 types. Through experiments on 1.000 frequently used character sets and 30.614 characters scanned from several magazines, I showed that the proposed method is more useful classifying Korean characters of huge set.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.61-78
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• 1991

This paper descibes the study of application of a modified Neocognitron model with backward path for the recognition of Hangul(Korean) syllabic characters. In this original report, Fukushima demonstrated that Neocognitron can recognize hand written numerical characters of $19{\times}19$ size. This version accepts $61{\times}61$ images of handwritten Hangul syllabic characters or a part thereof with a mouse or with a scanner. It consists of an input layer and 3 pairs of Uc layers. The last Uc layer of this version, recognition layer, consists of 24 planes of $5{\times}5$ cells which tell us the identity of a grapheme receiving attention at one time and its relative position in the input layer respectively. It has been trained 10 simple vowel graphemes and 14 simple consonant graphemes and their spatial features. Some patterns which are not easily trained have been trained more extrensively. The trained nerwork which can classify indivisual graphemes with possible deformation, noise, size variance, transformation or retation wre then used to recongnize Korean syllabic characters using its selective attention mechanism for image segmentation task within a syllabic characters. On initial sample tests on input characters our model could recognize correctly up to 79%of the various test patterns of handwritten Korean syllabic charactes. The results of this study indeed show Neocognitron as a powerful model to reconginze deformed handwritten charavters with big size characters set via segmenting its input images as recognizable parts. The same approach may be applied to the recogition of chinese characters, which are much complex both in its structures and its graphemes. But processing time appears to be the bottleneck before it can be implemented. Special hardware such as neural chip appear to be an essestial prerquisite for the practical use of the model. Further work is required before enabling the model to recognize Korean syllabic characters consisting of complex vowels and complex consonants. Correct recognition of the neighboring area between two simple graphemes would become more critical for this task.