• Proceedings of the IEEK Conference
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• 2003.07e
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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.

• Speech Sciences
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• v.8 no.2
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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.

• Phonetics and Speech Sciences
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• v.1 no.2
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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.

• Phonetics and Speech Sciences
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• v.1 no.2
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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.

• Korean Journal of Cognitive Science
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• v.12 no.3
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• pp.19-28
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• 2001

In this paper, we describe an algorithm for transliterated foreign word extraction in Korean language. In the proposed method we reformulate the transliterated foreign word extraction problem as a syllable-tagging problem such that each syllable is tagged with a transliterated foreign syllable tag or a pure Korean syllable tag. Syllable sequences of Korean strings ale modeled by Hidden Markov Model whose state represents a character with binary marking to indicate whether the character forms a Korean word or not. The proposed method extracts a transliterated foreign word with high recall rate and precision rate. Moreover, our method shows good performance even with small-sized training corpora.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.13-22
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• 2024

Currently, with the proliferation of digital devices, the significance of handwritten texts in daily lives is gradually diminishing. As the use of keyboards and touch screens increase, a decline in Korean handwriting quality is being observed across a broad spectrum of Korean documents, from young students to adults. However, Korean handwriting still remains necessary for many documentations, as it retains individual unique features while ensuring readability. To this end, this paper aims to implement an application designed to improve and correct the quality of handwritten Korean script The implemented application utilizes the CRAFT (Character-Region Awareness For Text Detection) model for handwriting area detection and employs the VGG-Feature-Extraction as a deep learning model for learning features of the handwritten script. Simultaneously, the application presents the user's handwritten Korean script's reliability on a syllable-by-syllable basis as a recognition rate and also suggests the most similar fonts among candidate fonts. Furthermore, through various experiments, it can be confirmed that the proposed application provides an excellent recognition rate comparable to conventional commercial character recognition OCR systems.

• Annual Conference on Human and Language Technology
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• 1999.10e
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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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• v.16 no.1
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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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• v.15 no.5
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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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.229-236
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• 2016

In this paper, we suggest new technology that can draw characters at a long distance by tracking a hand and analysing the trajectories of hand positions. It's difficult to recognize the shape of a character without discriminating effective strokes from all drawing strokes. We detect end points from input trajectories of a syllable with camera system and localize strokes by using detected end points. Then we classify the patterns of the extracted strokes into eight classes and finally into two categories of stroke that is part of syllable and not. We only draw the strokes that are parts of syllable and can display a character. We can get 88.3% in classification accuracy of stroke patterns and 91.1% in stroke type classification.