• MALSORI
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• no.1
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• pp.12-25
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• 1980

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.459-466
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• 2009

While Korean allows the same consonants at the coda of the preceding syllable and at the onset of the following syllable, English does not allow the geminate consonants in the same intervocalic position. Due to this difference between Korean and English, Korean learners of English tend to incorrectly produce geminate consonants for English geminate graphemes as in $su\underline{mm}er$. Based on this observation, a pilot study was designed to investigate how Korean learners of English perceive and produce English doubleton graphemes and singleton graphemes. Twenty Korean college students were asked to perform a forced-choice perception test as well as a production test for the 36 real word stimuli which consist of (near) minimal pairs of singleton and doubleton graphemes. The result showed that the accuracy rates for the words with singleton graphemes were higher than those for the words with doubleton graphemes both in perception and production because the subjects misperceived and misproduced the doubleton graphemes as geminates due to orthographic influence. In addition, the low error rates of the word with voiced stops were accounted for by Korean language transfer. Further, spectrographic analyses were provided where more production errors were witnessed in doubleton grapheme words than singleton grapheme words. Finally, pedagogical implications are provided.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.239-246
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• 2017

This paper compares the speech signal parameters between Korean and Chinese for Korean pronunciation /ㄹ/, which is caused many errors by Chinese leaners. Allophones of /ㄹ/ in Korean is divided into lateral group and tap group. It has been investigated the reasons for these errors by studying the similarity and the differences between Korean /ㄹ/ pronunciation and its corresponding Chinese pronunciation. In this paper, for the purpose of comparison the speech signal parameters such as energy, waveform in time domain, spectrogram in frequency domain, pitch based on ACF, Formant frequencies are used. From the phonological perspective the speech signal parameters such as signal energy, a waveform in the time domain, a spectrogram in the frequency domain, the pitch (F0) based on autocorrelation function (ACF), Formant frequencies (f1, f2, f3, and f4) are measured and compared. The data, which are composed of the group of Korean words by through a philological investigation, are used and simulated in this paper. According to the simulation results of the energy and spectrogram, there are meaningful differences between Korean native speakers and Chinese leaners for Korean /ㄹ/ pronunciation. The simulation results also show some differences even other parameters. It could be expected that Chinese learners are able to reduce the errors considerably by exploiting the parameters used in this paper.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1092-1096
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• 2009

While Korean allows the same consonants at the coda of the preceding syllable and at the onset of the following syllable, English does not allow the geminate consonant in the same position. Due to this difference between Korean and English, Korean learners of English tend to incorrectly produce geminate consonants for English geminate graphemes as in summer. Based on this observation, a pilot study was designed to investigate how Korean learners of English perceive and produce English doubleton graphemes and singleton graphemes. Twenty Korean college students were asked to perform a forced-choice perception test as well as a production test for the 36 real word stimuli which consist of near minimal pairs of singleton and doubleton graphemes. The result showed that the accuracy rates for the word with singleton graphemes were relatively high both in perception and production (78.6% and 76.1%, respectively), while those for the word with doubleton graphemes were low both in perception and production (55.3% and 61.7%, respectively). Also, spectrographic analyses were provided where more production errors were witnessed in doubleton grapheme words than singleton grapheme words.

• MALSORI
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• no.42
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• pp.47-56
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• 2001

이 글은 제2회 서울 국제 음성학 학술대회(SICOPS 2000) 기조강연 내용을 조금 손질한 것인데, 한국인 영어 학습자가 저지르기 쉬운 발음상 잘못을 모음, 자음별로 관찰하고 그 대책을 논의한다. 모음에서는 주로 i:l, u:$-\sigma$, (equation omitted) 흔동이 문제이며, 또한 90종이 넘는 여러 철자로 나타나는 쭉정모음(schwa) 식별과 정복한 발음도 큰 문제다. 자음에서는 음소 연결방식에서 생기는 자음접변 둥 한 국어 특유 현상을 영어에까지 연장하는 바람에 많은 오류가 생긴다는 것과 영어 sp-, st-, sk-에서 /p t k/는 연한소리(lenis)로 [(equation omitted)]인데, 된소리로 잘못알고 있는 수가 많다는 것도 지적된다. 무룻 영어학습자는 철자만 보고 발음을 속단하지 말고 단어마다 반드시 발음을 사전에서 확인할 것과 아울러 거기에 음성학적 훈련이 수반되어야 함을 역설하며, 정확한 발음을 아는 것은 실제 영어 청취i구사에 뿐 아니라 또한 언어연구 기초확립에 필수적이라는 말로 글을 맺는다.

• Proceedings of the KSPS conference
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• 1997.07a
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• pp.248-248
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• 1997

오늘날 영어교육은 교과과정령에 엄연히 명시된 네 가지 기능(four skills) 즉 듣기, 말하기, 원기, 쓰기라는 정당하고도 보편 타당성 있는 명분 하에 어떻게 가르쳐 왔는가 를 반문해 보면 많은 아쉬움이 남는다. 그간 6년간의 중등과정, 심지어는 대학에서 환 두해까지 영어를 이수한 사람틀 중에는 문자를 통해서는 상당한 수준, 그것도 영어 토박이들조차 놀랄 정도의 영어를 이해하지만, 소리를 통해 들을 때는 ---말하는 것은 두말 할 것도 없고---아주 간단한 내용의 영어조차 알아듣기 힘든 경험을 한 사람이 많다는 것은 부인할 수 없는 사실이다. 그 이유는 명백하다. 즉, 문자를 대할 때는 시각적 자극의 형태가 두뇌 속에 저장된 정보---가공할 문법적 지식---와 일치하기 때문에 쉽게 이해를 할 수 있는 반면, 소리를 들을 때는 청각적 자극의 형태가 두뇌 속에 저장된 정보---극히 불완전한 발음사전, 또는 모국어의 음운체계에 의한 영어발음--- 와 차이가 있기 때문일 것이다. 그러므로 적어도 말소리를 매체로 하는 의사소통에 있어서는 영어의 본토박이 발음을 정확히, 아니면 적어도 매우 근접하게 나마 터득하여(습관화하여)두뇌에 저장하는 일이 가장 중요한 일이다. 따라서 영어교사는 모국어의 음운체계에 대한 정확하고도 상세한 지식을 토대로 하여 영어의 음운체계와 '언어학적으로 의미 있는 (linguistically significant)' 대초분석의 방법으로 발음을 지도한다면 보다 나은 학습효과를 기대할 수 있을 것이다. 일반적으로 모국어의 발음이 외국어의 발음에 간섭을 유발하는 경우는 다음과 같다. 1. 분절음체계가 서로 다를 때 2. 한 언어의 음소가 다른 언어의 이음(allophone)일 때 3. 유사한 음의 조음장소와 방법 이 다를 때 4. 분절음의 분포 또는 배열이 다를 때 5. 음운현상이 다를 때 6. 언어의 리듬이 다를 때 위의 여섯 가지 경우를 중심으로 영어와 한국어의 발음특성을 대조하여 '낯선 말투(foreign accent)' 또는 발음오류를 최소로 줄이는 것이 영어교사의 일차적인 목표이다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.267-278
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• 2017

As the same letter can be pronounced differently depending on word contexts, one should refer to a lexicon in order to pronounce a word correctly. Phonetic alphabets that lexicons adopt as well as pronunciations that lexicons describe for the same word can be different from lexicon to lexicon. In this paper, we use a sequence-to-sequence model that is widely used in deep learning research area in order to convert automatically from one pronunciation to another. The 12 seq2seq models are implemented based on pronunciation training data collected from 4 different lexicons. The exact accuracy of the models ranges from 74.5% to 89.6%. The aim of this study is the following two things. One is to comprehend a property of phonetic alphabets and pronunciations used in various lexicons. The other is to understand characteristics of seq2seq models by analyzing an error.

• Language Facts and Perspectives
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• v.44
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• pp.357-397
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• 2018

The purpose of this paper is to investigate the phonemic errors in Korean learner's spoken corpus. Through this, we tried to investigate the common errors and the errors in certain languages. The results of the analysis were as follows. First, Errors that distinguish three phonemes(plain sound, tense sound, aspiration sound) were high in all languages. In the middle phonemes, the most common errors in pronouncing 'ㅓ' in all languages. Second, the errors of each language are different. Comparing the ratios by position, Chinese characters had the most common errors with 50% in final phoneme, and the Japanese language showed equal errors in initial, middle, and end. In English, initial phoneme errors accounted for 58%. Vietnamese Learners showed intensive errors in the initial and final phoneme. Third, in addition to the phoneme errors, we also examined the allophone errors and foreign language pronunciation errors. The allophone errors are mainly concentrated in 'ㄹ', ​​and the pronunciation of the foreign language is mainly used in the source language or the native language of the learners. This paper analyzes the phoneme errors in the Learner's spoken language through the spoken corpus data with representative and annotation consistency. Through this study, we could compare the difference of phoneme errors of Main Korean learners.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1362-1365
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• 2013

한국어 텍스트에 나타나는 오류어의 유형은 크게 단순 철자오류와 문맥 철자오류로 구분할 수 있다. 이중 문맥 철자오류는 문맥의 의미 통사적 관계를 고려해야만 해당 어휘의 오류 여부를 알 수 있는 오류로서 철자오류 중 교정 난도가 가장 높다. 문맥 철자오류의 유형은 발음 유상성에 따른 오류, 오타 오류, 문법 오류, 띄어쓰기 오류로 구분할 수 있다. 본 연구에서는 오타 오류에 의해 발생하는 문맥 철자오류를 어의 중의성 해소와 같은 문제로 보고 교정 어휘 쌍을 이용한 통계적 문맥 철자오류 교정 방법을 제안한다. 미리 생성한 교정 어휘 쌍을 대상으로 교정 어휘 쌍의 각 어휘와 주변 문맥 간 의미적 연관성을 통계적으로 측정하여 문맥 철자오류를 검색하고 교정한다. 제안한 방법을 적용한 결과 3개의 교정 어휘 쌍 모두 90%를 넘는 정확도를 보였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.197-205
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• 2020

Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.