• MALSORI
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• no.49
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• pp.123-134
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• 2004

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. In this paper we propose an HMM based speech recognizer which automatically classifies pronunciation errors when Koreans speak Japanese. We also propose two pronunciation networks for automatic detection of mispronunciation. In this paper, we evaluated performances of the networks by computing the correlation between the human ratings and the machine scores obtained from the speech recognizer.

• MALSORI
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• no.46
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• pp.87-102
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• 2003

In this paper, we present an English pronunciation correction system for Korean speakers and show some of experimental results on it. The aim of the system is to detect mispronounced phonemes in spoken words and to give appropriate correction comments to users. There are several English pronunciation correction systems adopting speech recognition technology, however, most of them use conventional speech recognition engines. From this reason, they could not give phoneme based correction comments to users. In our system, we build two kinds of phoneme models: standard native speaker models and Korean's error models. We also design recognition network based on phonemes to detect Koreans' common mispronunciations. We get 90% detection rate in insertion/deletion/replacement of phonemes, but we cannot get high detection rate in diphthong split and accents.

• The Journal of Korean Association of Computer Education
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• v.18 no.4
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• pp.11-18
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• 2015

A person with defects and the lack of hearing, has very difficult problems to listen to the sound of conversation or environment, and these problems cause wrong directions in the linguistic sense and character. We designed and implemented an application to practice and correct pronunciation of a hearing-impaired person specially. This is made as a mobile app without the limitation of time and space, and this provides practices with the level of difficulty in learning of pronunciation, taking into consideration of remedial status in pronunciation. The hearing-impaired person can train the basic pronunciation and the word pronunciation. He or she can practice basic skills in pronunciation and check the record on the practice rate and the success rate. Also, the person can improve pronunciation by self-correction function using the front camera of a smart phone.

• 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.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.29-32
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• 2003

This paper proposes an automatic pronunciation correction system which provides users with correction guidelines for each pronunciation error. For this purpose, we develop an HMM speech recognizer which automatically classifies pronunciation errors when Korean speaks foreign language. And, we collect speech database of native and nonnative speakers using phonetically balanced word lists. We perform analysis of mispronunciation types from the experiment of automatic mispronunciation detection using speech recognizer.

• 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.

• MALSORI
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• no.48
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• pp.127-139
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• 2003

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. In this paper we propose an HMM based speech recognizer which automatically classifies pronunciation errors when Korean speak Japanese. For this purpose we also develop phoneme recognizers for Korean and Japanese. Experimental results show that the machine scores of the proposed recognizer correlate with expert ratings well.

• Phonetics and Speech Sciences
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• v.5 no.2
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• pp.81-90
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• 2013

The purposes of this study are to: (1) Compare the vowel production of English front vowels produced by Korean speakers using regional dialects and; (2) Investigate and compare the effectiveness of pronunciation training for each regional dialect group. To test these objectives, the English front vowels produced by five Youngnam dialect male speakers, five Youngnam dialect female speakers, five Kangwon dialect male speakers, and five Kangwon dialect female speakers were scrutinized. These dialect groups' vowel formants and length of English front vowels were evaluated, and the post-pronunciation training values were compared with those of pre-training values. The results indicate that pronunciation training is more effective for Youngnam dialect speakers, whilst both dialect groups have more success mastering the pronunciation of /${\varepsilon}$/ over /${\ae}$/.