• The Journal of the Acoustical Society of Korea
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• v.10 no.6
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• pp.5-11
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• 1991

연속음 인식을 위하여 연결된 음향 신호를 음소단위로 분할하는 것이 필요하다. 본 논문에서는 연속 음성에서의 정상상태 구간 검출을 위한 파라미터로서 진폭분포를 이용하는 방법을 제안하였다. 제 안된 진폭분포는 음성신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진폭분포를 이용하는 방 법을 제안하였다. 제안된 지폭분포는 음성 신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진 폭 분포 차이값을 비교하여 프레임의 안정구간과 천이구간을 구분할 수 있었다.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.235-241
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• 2017

The Effects of the Story Character Phonics on Preschoolers' Early Reading Abilities and Affective Domains The purpose of this study was to investigate the effects of the story character phonics on preschoolers' early reading abilities (phonemic awareness and phonics abilities) and their affective domains. 24 participants in the present study were seven years old, and they were divided into two different groups at S kindergarten in Chungnam. There were 12 preschoolers in the experimental group with the story character phonics, and 12 preschoolers in the control group learned English by the story phonics focusing on phonemic awareness and vocabulary for 6 weeks. The results were as follows: First, the story character phonics was more effective in improving the preschoolers' early reading abilities than using the story phonics. Secondly, the story character phonics had some positive effects on the preschoolers' affective domains. This study proved that practicing with the story character phonics is more effective for preschoolers to develop their early reading abilities of English and their affective domains.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.454-459
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• 1998

연속된 음성의 인식을 위해서는 음성신호를 음성학적인 단위인 단어, 음절, 음소 등으로 분할하여야 한다. 이러한 분할을 위해서는 전이구간의 검출이 선행되어야 한다. 본 논문에서는 음성신호에서 전이구간을 검출하기 위해 피치동기로 된 상관관계 계수의 변화를 나타내는 파라미터를 새로이 제안하였다. 이 파라미터는 음성신호의 안정구간에서는 매우 작은 값을 나타내지만 음성의 시작이나 유성음과 무성음의 경계에서는 큰 값을 나타내어 전이구간검출용 파라미터로 매우 용이하다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.315-319
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• 1998

대어휘 연속음성인식을 위한 확률 발음사전 모델에 대해서 제안하였다. 제안된 확률 발음 사전은 연속음성과 같은 자연스런 발성에서 자주 발생되는 단어의 변이를 확률적인 subword-state로 이루어진 HMM으로 모델화 함으로써 단어의 발음 변이를 효과적으로 표현할 수 있으며, 단위 인식 시스템의 성능을 보다 높일 수 있도록 구성되었다. 확률 발음사전의 생성은 음성 자료와 음소 모델을 이용하여 단어 단위의 분할과 학습을 통해서 자동으로 생성되게 됨 음소와 같은 언어학적인 단위뿐만 아니라 PLU 이나 비언어학적인 인식 모델을 이용한 연속음성인식기에도 적용이 가능하다.연속음성인식실험결과 확률 발음사전을 사용함으로써 표준 발음 표기를 사용하는 인식 시스템에 비해 단어 오류율은 39.8%, 문장 오류율은 24.4%의 큰 폭으로 오류율을 감소시킬 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• Phonetics and Speech Sciences
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• v.11 no.4
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• pp.19-33
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• 2019

This paper examines the perception of Korean phonemes by Egyptian learners of Korean and presents the learnability gradient of Korean consonants and vowels through High Variability Phonetic Training (HVPT). 50 Egyptian learners of Korean (27 low proficiency learners and 23 high proficiency learners) participated in 10 sessions of HVPT for Korean vowels, word initial and final consonants. Participants were tested on their identification ability of Korean vowels, word initial consonants, and syllable codas before and after the training. The results showed that both low and high proficiency groups did benefit from the training. Low proficiency learners showed a higher improvement rate than high proficiency learners. Based on the HVPT results, a learnability gradient was established to give insights into priorities in teaching Korean sounds to Egyptian learners.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.29-32
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• 2000

본 논문에서는 음성인식 시스템의 음향모델 개선을 위한 기초적 연구로서, 문맥적인 요소를 필요로 하는 SSS(Successive State Splitting)와 필요로 하지 않는 SSS-free 알고리즘을 이용한 HMnet(Hidden Markov Network) 음향모델 작성방법에 대해 검토하고 작성한 음향모델을 한국어에 적용하여 그 유효성을 확인하였다. HMnet을 이용한 음소모델의 작성방법은 전체 학습 데이터에 대해서 각각 2개의 상태를 가지는 초기 모델을 작성한 후, 이를 시간과 문맥방향으로의 최대 분포를 가지는 상태를 재분할한 후 임의의 상태수가 될 때까지 상태분할을 계속적으로 수행케 하여 각 음소모델을 작성하게 된다. 작성한 HMnet 음향모델의 유효성을 확인하기 위해 ETRI 445 단어의 3인에 대한 화자종속 음소인식 실험을 수행하였다. 인식실험 결과, SSS 알고리즘을 이용한 화자종속실험의 경우 상태수 520에서 평균 $62.8\%$의 인식률을, SSS-free 알고리즘의 경우 상태수 420에서 평균 $64.2\%$의 인식률을 얻었다. 이 결과는 HMM을 이용한 경우(약$43.4\%$)보다 $20\%$이상의 인식률 향상을 보여 이 알고리즘의 유효성을 확인할 수 있었다. SSS와 SSS-free를 비교한 경우, SSS-free가 SSS보다 낮은 상태수에서 평균 $1.4\% 향상된 인식률을 보였다.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.199-210
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• 2002

In this paper, we carried out the study on speech recognition using the KM-Net topology design algorithm based on decision tree state-clustering to improve the performance of acoustic models in speech recognition. The Korean has many allophonic and grammatical rules compared to other languages, so we investigate the allophonic variations, which defined the Korean phonetics, and construct the phoneme question set for phonetic decision tree. The basic idea of the HM-Net topology design algorithm is that it has the basic structure of SSS (Successive State Splitting) algorithm and split again the states of the context-dependent acoustic models pre-constructed. That is, it have generated. the phonetic decision tree using the phoneme question sets each the state of models, and have iteratively trained the state sequence of the context-dependent acoustic models using the PDT-SSS (Phonetic Decision Tree-based SSS) algorithm. To verify the effectiveness of the above algorithm we carried out the speech recognition experiments for 452 words of center for Korean language Engineering (KLE452) and 200 sentences of air flight reservation task (YNU200). Experimental results show that the recognition accuracy has progressively improved according to the number of states variations after perform the splitting of states in the phoneme, word and continuous speech recognition experiments respectively. Through the experiments, we have got the average 71.5%, 99.2% of the phoneme, word recognition accuracy when the state number is 2,000, respectively and the average 91.6% of the continuous speech recognition accuracy when the state number is 800. Also we haute carried out the word recognition experiments using the HTK (HMM Too1kit) which is performed the state tying, compared to share the parameters of the HM-Net topology design algorithm. In word recognition experiments, the HM-Net topology design algorithm has an average of 4.0% higher recognition accuracy than the context-dependent acoustic models generated by the HTK implying the effectiveness of it.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2220-2223
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• 2003

In this paper we used Support Vector Machines(SVMs) recently proposed as the loaming method, one of Artificial Neural Network, to divide continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme divided by eye-measurement. From experiment we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.179-186
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• 2005

We can extract spectrum of the voices and analyze those, after employing features of frequency that voices have. In the spectrum of the voices monophthongs are thought to be stable, but when a consonant(s) meet a vowel(s) in a syllable or a word, there is a lot of changes. This becomes the biggest obstacle to phoneme speech recognition. In this study, using Mel Cepstrum and Mel Band that count Frequency Band and auditory information, we analyze the spectrums that each and every consonant and vowel has and the changes in the voices reftects auditory features and make it a system. Finally we are going to present the basis that can segment the voices by an unit of phoneme.