• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.27-36
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• 2016

Recognition of grapheme is a very important process in the recognition within 'Hangul(Korean written language)' letters using phoneme recognition. It is because the success or failure in the recognition of phoneme greatly affects the recognition of letters. For this reason, it is reported that separation of phonemes is the biggest difficulty in the phoneme recognition study. The current study separates and suggests the new phonemes that used the connective elements that are helpful for dividing phonemes, recommends the features for recognition of such suggested phonemes, databases this, and carried out a set of experiments of recognizing phonemes using the suggested features. The current study used 350 letters in the experiment of phoneme separation and recognition. In this particular kind of letters, there were 1,125 phonemes suggested. In the phoneme separation experiment, the phonemes were divided in the rate of 100%, and the phoneme recognition experiment showed the recognition rate of 98% in recognizing only 14 phonemes into different ones.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.4
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• pp.33-41
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• 1997

Korean unvoiced phonemes consist of nonstationary parts comparing that the vowels and nasal consonants consist of quasi-stationary part. And some phonemes, which have smae point of articulation but differnt manner of articulation, has similar characteristics, so it makes to be hard to distinguish each other. A new method usin gchanges and characteristics of acoustic properties of these phonemes to improve recognition rate are proposed. And because these changes and cahracteristics evidently occur in continuous speech except some unvoiced consonants are articulated as voiced phoneme in case to be used as an midial between voiced phonemes, this method can be applied easily. The features of the frames extracted to represent each phonemes are used asinputs to the hierarchical neural network. And with these results final decision for phoneme recognition is made thorugh post processing which the new method is applied to. Through the experimental recognition results for 9 unvoiced consonants which belong to bilabial, alveolar, and velar phoneme series, 89.4% recognition rate to distinguish in same phoneme series is obtained, and 85.6% recognition rate is obtained in case of including cistinguishing phoneme series.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.9-16
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• 2019

Computer-Assisted Pronunciation Training system is considered to be a useful tool for pronunciation learning for students who received elementary level English pronunciation education, especially for students who have difficulty in correcting their pronunciation in front of others or who are not able to receive face-to-face training. The conventional Computer-Assisted Pronunciation Training system shows the word to the user, the user pronounces the word, and then the system provides phoneme or audio feedback according to the pronunciation of the user. In this paper, we propose a Computer-Assisted Pronunciation Training system that can practice on the varying pronunciation according to positions of adjacent phonemes. To achieve this, the proposed system is implemented by recommending a series of words by focusing on adjacent phonemes for simplicity and clarity. Experimental results showed that word recommendation considering adjacent phonemes leads to improvement of pronunciation accuracy.

• Proceedings of the KSPS conference
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• 1997.07a
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• pp.142-143
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• 1997

The purpose of this paper is to assess the psychological status of the phoneme, syllable, and various postulated subsyllabic units in Korean by applying the Sound Similarity Judgment (SSJ) task, to compare the results with those in English, and to discuss the advantage and disadvantage of the SSJ task as a tool for linguistic research. In Experiment 1, 30 subjects listened to pairs of 56 eve words which were systematically varied from 'totally different' (e.g., pan-met) to 'identical' (e.g., pan-pan). Subjects were then asked to rate sound similarity of each pair on a 10-point scale. Not very surprisingly, there was a strong correlation between the number of phonemic segments matched and the similarity score provided by the subjects. This result was in accord with the previous results from English (e.g., Vitz & Winkler, 1973; Derwing & Nearey, 1986) and supported the assumption that the phoneme is the basic phonological unit in Korean and English. However, there were sharply contrasting results between the two languages. When the pairs shared two phonemes (e.g., pan-pat; pan-pen; pan-man), the pairs sharing the fIrst two phonemes were judged significantly more similar than the other two types of pairs. Quite to the contrary, in the comparable English experiments, the pairs sharing the last two phonemes were judged significantly more similar than the other two types of pairs. Experiment 2 was designed to conflrm the results of Experiment 1 by controlling the 'degree' of similarity between phonemes. For example, the pair pan-pam can be judged more similar than the pair pan-nan, although both pairs share the same number of phonemes. This could be interpreted either as confirming the result of Experiment 1 or as the fact that /n/ is more similar to /m/ than /p/ is to /n/ in terms of shared number of distinctive features. The results of Experiment 2 supported the former interpretation. Thus, the results of both experiments clearly showed that, although the 'number' of matched phonemes is the important predictor in judging sound similarity of monosyllabic pairs of both languages, the 'position' of the matched phonemes exerts a different influence in judging sound similarity in the two languages. This contrasting set of results may provide interesting implications for the internal structure of the syllable in the two languages.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.174-182
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• 1996

In continuous speech recognition system, it is possible to implement the system which can handle unlimited number of words by using limited number of phonetic units such as phonemes. Dividing continuous speech into the string of tems of phonemes prior to recognition process can lower the complexity of the system. But because of the coarticulations between neiboring phonemes, it is very difficult ot extract exactly their boundaries. In this paper, we propose the algorithm ot extract short terms which can represent each phonemes instead of extracting their boundaries. The short terms of lower spectral change and higher spectral chang eare detcted. Then phoneme changes are detected using distance measure with this lower spectral change terms, and hgher spectral change terms are regarded as transition terms or short phoneme terms. Finally lower spectral change terms and the mid-term of higher spectral change terms are regarded s the represent each phonemes. The cepstral coefficients and weighted cepstral distance are used for speech feature and measuring the distance because of less computational complexity, and the speech data used in this experimetn was recoreded at silent and ordinary in-dorr environment. Through the experimental results, the proposed algorithm showed higher performance with less computational complexity comparing with the conventional segmetnation algorithms and it can be applied usefully in phoneme-based continuous speech recognition.

• Speech Sciences
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• v.3
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• pp.148-155
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• 1998

As an application of dimensional analysis in the theory of chaos and fractals, we studied and estimated the information dimension for various phonemes. By constructing phase-space vectors from the time-series speech signals, we calculated the natural measure and the Shannon's information from the trajectories. The information dimension was finally obtained as the slope of the plot of the information versus space division order. The information dimension showed that it is so sensitive to the waveform and time delay. By averaging over frames for various phonemes, we found the information dimension ranges from 1.2 to 1.4.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1985.10a
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• pp.109-112
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• 1985

This paper proposes a algorithm of phonemes extraction in korean character pattern recognition. The phonemes are classified into the patterns which are separable and connected with each other. The former is extracted by means of pattern matching in consideration of topological structure of ponemes and direction of stroke sequentially. The latter is extracted by means of index and window algorithm which are performed by a 3$\times$3 sequential local operation in the thinned character pattern.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.22-30
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• 1992

Using Multi-Dimentional Scaling(MDS) method, this paper analyzes the differences of acoustic properties of Korean phonemes projected as distances on a plan space. The phonemes were extracted from mono-syllables frequently occurring in daily conversation. From the distances between vowels we found that the distances between vowels /∂/ and /w/, /o/ and /u/, and vowels /$\varepsilon$/ and /e/ were relatively too short separate automatically. From the analysis of consonants. we found short distances between 1) phonemes in each phoneme group, 2) nasals and liquid /r/ of work initial, 3) nasal /n,m/ and liquid /l/ of word finals. But nasals, liquids and plosives of word initials had enough distances to be separated from those of word finals in automatic recogniation.

• Speech Sciences
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• v.7 no.4
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• pp.27-40
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• 2000

The goal of quantitative linguistics is to show the quantitative behavior of linguistic units. There are several studies which examine the frequency of Korean phonemes, which are important in comprehending the internal function of the linguistic units. However, the frequency information, from the pure phonological level without any consideration of rhythmic group, cannot adequately represent linguistic phenomena. Therefore, to provide the effective information, the phonological transcription must be carried out on the level of rhythmic group. In this paper, we made the transcription to analyze Korean phonology. We were not satisfied with merely investigating the frequencies of the phonemes, but also examined whether the distribution of Korean phonemes show the binomial distribution within linguistic constraints.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.14-23
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• 1996

This paper is a on the neural network to recognize the phonemes, the weight initialization to reduce learning speed, and LR parser for continuous speech recognition. The neural network spots the phonemes in continuous speech and LR parser parses the output of neural network. The whole phonemes recognized in neural network are divided into several groups which are grouped by the similarity of phonemes, and then each group consists of neural network. Each group of neural network to recognize the phonemes consisits of that recognize the phonemes of their own group and VGNN(Verify Group Neural Network) which judges whether the inputs are their own group or not. The weights of neural network are not initialized with random values but initialized from learning data to reduce learning speed. The LR parsing method applied to this paper is not a method which traces a unique path, but one which traces several possible paths because the output of neural network is not accurate. The parser processes the continuous speech frame by frame as accumulating the output of neural network through several possible paths. If this accumulated path-value drops below the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the continuous Korea digits recognition. The recognition rate of isolated digits is 97% in speaker dependent, and 75% in speaker dependent. The recognition rate of continuous digits is 74% in spaker dependent.