• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• 음성과학
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• 제8권2호
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• pp.83-90
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• 2001

We proposed a probabilistic approach of using syllable bigrams to the word-spacing problem. Syllable bigrams are extracted and the frequencies are calculated for the large corpus of 12 million words. Based on the syllable bigrams, we performed three experiments: (1) automatic word-spacing, (2) detection and correction of word-spacing errors for spelling checker, and (3) automatic insertion of a space at the end of line in the character recognition system. Experimental results show that the accuracy ratios are 97.7 percent, 82.1 percent, and 90.5%, respectively.

• 말소리와 음성과학
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• 제1권2호
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• pp.15-22
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• 2009

Prominence of speech, which is often called 'accent,' affects the fluency of speaking American English greatly. In this paper, we present an accurate prominence detection method that can be utilized in computer-aided language learning (CALL) systems. We employed pitch movement, overall syllable energy, 300-2200 Hz band energy, syllable duration, and spectral and temporal correlation as features to model the prominence of speech. After the features for vowel syllables of speech were extracted, prominent syllables were classified by SVM (Support Vector Machine). To further improve accuracy, the differences in characteristics of neighboring syllables were added as additional features. We also applied a speech recognizer to extract more precise syllable boundaries. The performance of our prominence detector was measured based on the Intonational Variation in English (IViE) speech corpus. We obtained 84.9% accuracy which is about 10% higher than previous research.

• 말소리와 음성과학
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• 제1권2호
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• pp.85-94
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• 2009

The purpose of this study was to see the effects of pitch and length patterns on spoken word recognition. In Experiment 1, a syllable monitoring task was used to see the effects of pitch and length on the pre-lexical level of spoken word recognition. For both Seoul dialect speakers and Daegu dialect speakers, pitch and length did not affect the syllable detection processes. This result implies that there is little effect of pitch and length in pre-lexical processing. In Experiment 2, a lexical decision task was used to see the effect of pitch and length on the lexical access level of spoken word recognition. In this experiment, word frequency (low and high) as well as pitch and length was manipulated. The results showed that pitch and length information did not play an important role for Seoul dialect speakers, but that it did affect lexical decision processing for Daegu dialect speakers. Pitch and length seem to affect lexical access during the word recognition process of Daegu dialect speakers.

• 인지과학
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• 제12권3호
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• pp.19-28
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• 2001

본 논문에서는 한국어문서에서 음차표기된 외래어를 자동적으로 인식 및 추출하는 알고리즘을 제안한다. 제안된 방법에서는 음차표기된 외래어 인식 및 추출 문제를 음절태깅문제로 변환한다. 음절태깅문제는 주어진 단어 내의 음절들에 대하여 순수 한국어를 구성하는 음절인지 또는 음차표기된 외래어를 구성하는 음절인지를 태깅하는 작업으로 정의된다. 이를 위하여. 주어진 어절 내의 음절의 나열을 순수 한국어 음절을 표현하는 상태와 외래어 음절을 표현하는 상태의 이진 상태(binary state)로 모델링한 은닉 마르코프 모델을 이용한다. 제안된 방법은 기존 연구에 비하여 높은 재현율과 정확률로 음차표기된 외래어를 인식 및 추출하였다.

• 한국산업정보학회논문지
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• 제29권3호
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• pp.13-22
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• 2024

현재 디지털 기기의 확산과 함께 일상에서 손으로 쓰는 글씨의 비중은 점점 줄어들고 있다. 키보드와 터치스크린의 활용도 증가에 따라 한글 필기체의 품질 저하는 어린 학생부터 성인까지 넓은 범위의 한글 문서에서 관찰되고 있다. 그러나 한글 필기체는 여전히 개인적인 고유한 특징을 포함하면서 가독성을 제공하는 많은 문서 작성에 필요하다. 이를 위해 본 논문에서는 손으로 쓴 한글 필기체의 품질을 개선하고, 교정하기 위한 목적의 어플 구현을 목적으로 한다. 제안된 어플은 CRAFT(Character-Region Awareness For Text Detection) 모델을 사용하여 필기체 영역을 검출하고, 딥러닝으로서 VGG-Feature-Extraction 모델을 사용하여 필기체의 특징을 학습한다. 이때 사용자가 작성한 한글 필기체의 음절 단위로 신뢰도를 인식률로 제시하고, 또한, 후보 폰트들중에서 가장 유사한 글자체를 추천하도록 구현한다. 다양한 실험을 통해 제안한 어플은 기존의 상용화된 문자 인식 소프트웨어와 비교할만한 우수한 인식률을 제공함을 확인할 수 있다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1999년도 제11회 한글 및 한국어 정보처리 학술대회
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• pp.335-342
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• 1999

The experiments reported here investigated whether seeing a speaker's face (visible speech) affects the perception and memory of Korean speech sounds. In order to exclude the possibility of top-down, knowledge-based influences on perception and memory, the experiments tested people with no knowledge of Korean. The first experiment examined whether visible speech (Auditory and Visual - AV) assists English native speakers (with no knowledge of Korean) in the detection of a syllable within a Korean speech phrase. It was found that a syllable was more likely to be detected within a phrase when the participants could see the speaker's face. The second experiment investigated whether English native speakers' judgments about the duration of a Korean phrase would be affected by visible speech. It was found that in the AV condition participant's estimates of phrase duration were highly correlated with the actual durations whereas those in the AO condition were not. The results are discussed with respect to the benefits of communication with multimodal information and future applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1119-1130
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• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• 한국정보전자통신기술학회논문지
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• 제9권2호
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• pp.229-236
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• 2016

본 논문에서는 3차원 공간에서 손 위치를 추적하고 움직임 궤적을 분석하여 원거리에서 판서가 가능한 기술을 제안한다. 3차원 공간에서 손으로 입력하는 한글 음절은 글자 획과 이동 획이 구분되지 않아 음절의 종류를 구분하기 힘들다. 이에 본 논문에서는 한글 음절을 구성하는 획을 글자 획과 이동 획으로 구분한 후 이동 획은 제거하고 글자 획만을 출력하는 방법을 제안한다. 우선, 필기체 음절의 궤적에서 획의 끝 점을 검출하고, 검출한 끝 점 정보를 이용하여 입력 음절을 획 단위로 분리한다. 음절 집합으로부터 8가지의 획 패턴을 정의한 후 분리한 획에 대해서는 방향 코드를 기반으로 획 패턴을 분류한다. 그리고 이를 기반으로 최종적으로 획의 유형을 글자 획과 이동 획으로 분류한다. 분류된 획의 유형을 기반으로 입력된 음절에서 이동 획은 제거하고 글자 획만을 출력하여 가독성이 있는 음절 표시가 가능하도록 한다. 360개의 음절 집합에 대해 정확도를 측정하여 획의 패턴은 88.3%, 획의 유형 구분은 91.1%의 정확도를 얻었다.