• The korean journal of orthodontics
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• v.21 no.2 s.34
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• pp.287-307
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• 1991

This study aimed at examining speech defects of openbite patients, which were analized in terms of formant frequency for vowels and word pronunciation length for consonants. In addition, the upper and lower lip (perioral m.) activity was tested by the EMG. The tongue force was measured by the strain gauge, and the speech discrimination test was carried out. One experimental group and one control group were used for this study and they were respectively composed of six female openbite patients and six normal-occlusion females. Eight monophthongs, two fricatives and two affricatives were chosen for speech analysis. Speeches of the above-mentioned groups were recorded and then analized by the ILS/PC-1 software. Four hundred most frequently used monosyllables were also chosen for discrimination score. Openbite patients showed the following characteristics: 1. Abnormality in case of /a/, $/\varepsilon/$, /e/, /i/ $F_2$ and /e/, /a/ $F_1$. 2. Significantly elongated length in their pronunciation of /h/ and $/C^h/$ and somewhat elongated length also in their pronunciation of /s/ and /c/. 3. Significant upper lip activity according to the EMG test during pronunciation of the bilabial consonants. 4. Relatively weak tongue force according to the strain gauge measurement. 5. According to the speech discrimination test, high rate of misarticulation in case of (a) initial /p/ /s'/ and /ts'/, (b) /a/,$/\varepsilon/$,/e/,/je/,/o/, $/\phi/$,/jo/,/u/,/we/, and /i/ (c) final (equation omitted).

• English Language & Literature Teaching
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• v.13 no.2
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• pp.1-23
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• 2007

The purpose of this research is to explore the effects of systematic teaching English pronunciation and listening in English. Focusing on phonemes and words in pairs and sentences, the sound systems of the English and Korean languages are dealt with in conjunction with the test data. This paper first discusses the systemic, or primary interference and the habitual, or secondary interference that hinder comprehension of certain English sounds. Second, the analysis of input and output test data on the contrasting vowels and consonants shows statistic significance in terms of the probability (p value) of t-test. Third, the comparative data by means of percentile of right answers on contrasting vowel and consonant sounds expound the different sound systems of the English and Korean languages. With this data, problems in pronunciation of and listening to English, and the factors that may cause these problems are analyzed so that they can be used as a guideline for a systematic approach in teaching English learners, thus leading to more satisfactory performance.

• MALSORI
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• no.49
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• pp.95-105
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• 2004

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. For this purpose we develope a speech recognizer which automatically classifies pronunciation errors when Koreans speak a foreign language. In order to develope the methods for automatic assessment of pronunciation quality, we propose a language model based score as a machine score in the speech recognizer. Experimental results show that the language model based score had higher correlation with human scores than that obtained using the conventional log-likelihood based score.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.104-111
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• 2007

This paper examines how the performance of an automatic speech recognizer was improved for Korean Point-of-Interest (POI) data by modeling pronunciation variation using structural prosodic information such as prosodic words and syllable length. First, multiple pronunciation variants are generated using prosodic words given that each POI word can be broken down into prosodic words. And the cross-prosodic-word variations were modeled considering the syllable length of word. A total of 81 experiments were conducted using 9 test sets (3 baseline and 6 proposed) on 9 trained sets (3 baseline, 6 proposed). The results show: (i) the performance was improved when the pronunciation lexica were generated using prosodic words; (ii) the best performance was achieved when the maximum number of variants was constrained to 3 based on the syllable length; and (iii) compared to the baseline word error rate (WER) of 4.63%, a maximum of 8.4% in WER reduction was achieved when both prosodic words and syllable length were considered.

• Proceedings of the KSPS conference
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• 2004.05a
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• pp.239-242
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we propose a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we also propose machine scoring methods for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• Speech Sciences
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• v.7 no.2
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• pp.245-253
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• 2000

In this paper, we implement an elementary system to correct accents and pronunciations in English words spoken by non-native English speakers. In case of the accent evaluation, energy and pitch information are used to find stressed syllables, and then we extract the segment information of input patterns using a dynamic time warping method to discriminate and evaluate accent position. For the pronunciation evaluation, we utilize the segment information using the same algorithm as in accent evaluation, and perform the spectral distance measure for each phoneme between input patterns and reference patterns. Based on these spectral distances, we decide whether to recommend the pronunciation correction or not. Our results show that 98 percent of accent and 71 percent of pronunciation evaluation agree with the perceptual measure.

• Speech Sciences
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• v.11 no.2
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• pp.217-226
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we develop a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we propose a machine scoring method for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.241-254
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• 2000

The past decade has witnessed the abundant use of computer in testing language skills such as listening and reading. Compared with these language skills, we have experienced little use of computer in testing a speaking skill including pronunciation. This is largely due to limitations of the current computer technology. One of such limitations for testing pronunciation is to store and automatically evaluate what the learner utters. Due to this limitation, the computer simply stores what the learner utters and raters evaluate it afterward on a certain rating continuum. With the advent of voice recognition technology, however, the computer has been able to test pronunciation in a systematic way. This technology enables the computer to identify, visually show, and evaluate the learner's intonation pattern by means of autocorrection. The evaluation is expressed in terms of the degree in which the learner's intonation pattern overlaps with that of the native speaker of the target language. In particular, the degree is numerically displayed on the screen, and this numeral is considered as the score of the learner's utterance under our testing framework.

• Speech Sciences
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• v.6
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• pp.33-44
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• 1999

This paper aims to analyze whether the phenomenon of /u/$\rightarrow$/i/ is a hypercorrection or not in the North Korean dialects. Most North Koreans pronounce /i/(gold) as /kum/ because the vowel /i/ merges into the peripheral vowel space of /u/ in their dialects. The merger of back vowel is one of most distinctive characters in North Korean dialects. But some speakers pronounce /chubann/(exile) as /chiban/. This time /u/ in peripheral space moves to /i/ in central vowel space. It seems that the vowels /i/ and /u/ exchange places with each other when they uttered in North Korean. Though it was observed that the vowel movement of /i/$\rightarrow$/u/ was caused by the merger of back vowels, the reason why vowel /u/ moves in the opposite direction, that is, the central space of vowel /i/ has not been analyzed yet. This experiment starts with hypothesis that the movement of /u/$\rightarrow$/i/ might be caused by hypercorrection. The first step of this research is to analyze /u/$\rightarrow$/i/ pronunciation of North Koreans. The second step is to compare the results of North Korean pronunciation with those of South Korean pronunciation and observe whether tendency of /u/$\rightarrow$/i/pronunciation can also be found in the standard Seoul dialect and other South Korean dialects.

• Journal of Korean Library and Information Science Society
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• v.25
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• pp.167-206
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• 1996

Some conclusions can be derived form the study : 1) The script of Japan author's name for 3 years(1960-1962, not published by the National Assembly Library but by Korea Library Association)and that of 1963's was arranged by their mother tongue although they had not the "author index". 2) "Periodical's Index" which the publication of National Assembly Library was not accept the principle that the person's name should be pronounce and script by one's mother tongue. It means that the Library was not accept the uniqueness of personal name. 3) Because the arrangement of the same person's name is mixed with one's mother tongue pronunciation and Korean one that they are scattered each another. 4) The same surname and the same Chinese character has different arrangement because of pronunciation rule of Korean language. 5) The same person's name was regarded as a different one because of nonaccurate name transcription. 6) A Japanese name was transcribed as Hangul with Korean pronunciation. 7) A Japanese name was transcribed as Hangul with Korean pronunciation and added Chinese Character in parenthesis. 8) A same Japanese name was regarded as a different one when it was transcribed with Chinese character and Hangul. 9) The arrangement of a same person's name was different when between the surname and forename has one space and has not. 10) "Author Index" is not playing as a role of name authority file.a role of name authority file.