• Proceedings of the KSPS conference
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• 대한음성학회 2000년도 7월 학술대회지
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• pp.178-178
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• 2000

This paper IS concerned with adults' foreign language aquisition and intends to research the relationship between the mother tongue's phonetic system (L1) and the perception of the foreign language (L2), in this paper Polish and Korean. The questions that are to help to define the aforementioned relationship are I) how Polish perceive Korean vowels, 2) how Koreans perceive Polish vowels, and 3) how Koreans perceive Korean vowels pronounced by Poles. In order to identify L2's vowels, the listeners try to fit them into the categories of their own language (L1). On the one hand, vowels that are the same in both languages and those that are articulated where no other vowel is articulated, have the best rate of recognition. For example, /i/ in both languages is a front close vowel and in both languages there are no other front close vowels. Therefore, vowels /i/ (and /a/) have the best rate of recognition in all three experiments. On the other hand, vowels that are unfamiliar to the listeners do not seem to have the worst rate of recognition. The vowels that have the worst rate of recognition are those, that are similar, but not quite the same as those of L1. This research proves that "equivalence classification prevents L2 learners from producing similar L2 phones, but not new L2 phones, authentically" (Flege, 1987). Polish speakers can pronounce unfamiliar L2 vowels "more authentically" than those similar to L1 vowels. However, the difference is not significant and this subject requires further research (different data, more informants).

• Proceedings of the KSPS conference
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• 대한음성학회 2002년도 11월 학술대회지
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• pp.51-59
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• 2002

This article discusses the teaching of listening and reading skills through enhancing the awareness of pronunciation. First, it examines the problems which take place in listening comprehension, and seeks the ways in which we can teach the skill rather than simply practise it. The approaches proposed are based on micro-listening exercises which practise individual subskills of listening, especially by using the cloze test and tracking. The issue of using authentic materials is then examined for teaching recognition of the features of natural speech. Finally, it is argued that classroom activities need to take account of the true nature of real-life L2 listening.

• MALSORI
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• 제63호
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• pp.101-112
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognition, language understanding, dialog management and knowledge management in a spoken dialog system, a developer should take an effort to edit corpus and train each model separately. To reduce a cost for editing corpus and training each model, we need more systematic and efficient working environment. For the working environment, we propose DialogStudio as a spoken dialog system workbench.

• Proceedings of the KSPS conference
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.207-210
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• 2003

In this paper, the combinations of speech enhancement techniques are experimented. Specifically, the spectral subtraction, KLT based comb-filtering, and their combinations are applied to the Aurora2 database. The results show that recognition accuracy is improved when KLT based comb-filtering is applied after spectral subtraction.

• Proceedings of the KSPS conference
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• 대한음성학회 2002년도 11월 학술대회지
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• pp.47-50
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• 2002

This paper is to introduce 'Dr. Speaking', which was recently developed by Eonon Inc.. 'Dr. Speaking' is an English pronunciation tutoring system. This has three distinguishing features. First, it teaches how to organize a speaker's vocal organs to pronounce accurately. Second, after it compares a speaker's pronunciation with that of a native speaker's, it grades that speaker's pronunciation level according to phonetic standards. Third, it provides proper information necessary for correcting a speaker's incorrect pronunciation. It is not always easy for a tutoring system to execute the above three almost simutaneously. However, 'Dr. Speaking' proved itself that it is possible by adding speech technology (e.g. speech recognition) to phonetic knowledge.

• Proceedings of the Acoustical Society of Korea Conference
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• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
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• pp.57-60
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• 2004

본 논문에서는 음성정보기술산업지원센터(SiTEC)에서 현재 배포중인 고소음 환경 음성 DB를 이용하여 air-conducted 음성과 body-conducted 음성의 인식 성능을 비교 실험하였다. 소음 환경에서 일반적인 마이크로폰으로부터 수집된 air-conducted 음성은 잡음의 영향을 받기 쉬우며 이는 인식률을 저하시킨다. 반면에 진동 픽업 마이크로폰에서 수집된 body-conducted 음성은 소음에 보다 강인한 특성을 보인다. 이러한 특성에 근거하여 소음 환경에서 일반 다이나믹 마이크로폰 음성에 음질 개선 방법과 채널 보상 방법을 적용한 인식 결과와 3종류의 진동 픽업 마이크로폰에서 수집된 음성과의 인식 성능을 비교 분석하여 body-conducted 음성 인식 시스템의 환용 가능성을 살펴보았다.

• Proceedings of the KSPS conference
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.41-44
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• 2005

This study is about Chinese tone evaluation system for Korean learners using speech technology, Chinese prounciaion system consists of initials, finals and tones. Initials/finals are in segmental level and tones are in suprasegmental level. So different method could be used assessing Korean users' Chinese. Differ from segmental level recognition method, we chose pattern matching method in evaluating Chinese tones. Firstly we defined speakers' own speech range and produced standard tonal pattern according to speakers' own range. And then we compared input patterns of users with referring patterns.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.218-227
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• 2018

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

• Proceedings of the KSPS conference
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• 대한음성학회 2004년도 춘계 학술대회 발표논문집
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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.

• Proceedings of the KSPS conference
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.95-98
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• 2005

Many utterance verification (UV) algorithms have been studied to reject out-of-vocabulary (OOV) in speech recognition systems. Most of conventional confidence measures for UV algorithms are mainly based on log likelihood ratio test, but these measures take much time to evaluate the alternative hypothesis or anti-model likelihood. We propose a novel confidence measure which makes use of a momentary best scored state sequence during Viterbi search. Our approach is more efficient than conventional LRT-based algorithms because it does not need to build anti-model or to calculate the alternative hypothesis. The proposed confidence measure shows better performance in additive noise-corrupted speech as well as clean speech.