• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.319-324
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• 2006

We Propose two new methods for non-keyword modeling to improve the performance of speaker- and vocabulary-independent keyword spotting system. The first method is decision tree clustering of monophone at the state level instead of monophone clustering method based on K-means algorithm. The second method is multi-state multiple mixture modeling at the syllable level rather than single state multiple mixture model for the non-keyword. To evaluate our method, we used the ETRI speech DB for training and keyword spotting test (closed test) . We also conduct an open test to spot 100 keywords with 400 sentences uttered by 4 speakers in an of fce environment. The experimental results showed that the decision tree-based state clustering method improve 28%/29% (closed/open test) than the monophone clustering method based K-means algorithm in keyword spotting. And multi-state non-keyword modeling at the syllable level improve 22%/2% (closed/open test) than single state model for the non-keyword. These results show that two proposed methods achieve the improvement of keyword spotting performance.

• Journal of Audiology & Otology
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• v.23 no.2
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• pp.112-117
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• 2019

Background and Objectives: The purpose of this study was to evaluate the efficacy of revision cochlear implant (CI) surgery for better speech comprehension targeting patients with low satisfaction after first CI surgery. Subjects and Methods: Eight patients who could not upgrade speech processors because of an too early CI model and who wanted to change the whole system were included. After revision CI surgery, we compared speech comprehension before and after revision CI surgery. Categoies of Auditory Performance (CAP) score, vowel and consonant confusion test, Ling 6 sounds, word and sentence identification test were done. Results: The interval between surgeries ranged from eight years to 19 years. Same manufacturer's latest product was used for revision surgery in six cases of eight cases. Full insertion of electrode was possible in most of cases (seven of eight). CAP score (p-value=0.01), vowel confusion test (p-value=0.041), one syllable word identification test (p-value=0.026), two syllable identification test (p-value=0.028), sentence identification test (p-value=0.028) had significant improvement. Consonant confusion test (p-value=0.063), Ling 6 sound test (p-value=0.066) had improvement but it is not significant. Conclusions: Although there are some limitations of our study design, we could identify the effect of revision (upgrade) CI surgery indirectly. So we concluded that if patient complain low functional gain or low satisfaction after first CI surgery, revision (device upgrade) CI surgery is meaningful even if there is no device failure.

• Korean Journal of Audiology
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• v.23 no.2
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• pp.112-117
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• 2019

Background and Objectives: The purpose of this study was to evaluate the efficacy of revision cochlear implant (CI) surgery for better speech comprehension targeting patients with low satisfaction after first CI surgery. Subjects and Methods: Eight patients who could not upgrade speech processors because of an too early CI model and who wanted to change the whole system were included. After revision CI surgery, we compared speech comprehension before and after revision CI surgery. Categoies of Auditory Performance (CAP) score, vowel and consonant confusion test, Ling 6 sounds, word and sentence identification test were done. Results: The interval between surgeries ranged from eight years to 19 years. Same manufacturer's latest product was used for revision surgery in six cases of eight cases. Full insertion of electrode was possible in most of cases (seven of eight). CAP score (p-value=0.01), vowel confusion test (p-value=0.041), one syllable word identification test (p-value=0.026), two syllable identification test (p-value=0.028), sentence identification test (p-value=0.028) had significant improvement. Consonant confusion test (p-value=0.063), Ling 6 sound test (p-value=0.066) had improvement but it is not significant. Conclusions: Although there are some limitations of our study design, we could identify the effect of revision (upgrade) CI surgery indirectly. So we concluded that if patient complain low functional gain or low satisfaction after first CI surgery, revision (device upgrade) CI surgery is meaningful even if there is no device failure.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.213-232
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• 2005

The purpose of this study is to determine factors affecting text difficulty and to model objective formulas which measure readability scores. Some readability-related factors such as total number of letters, total number of syllables, total number of unique syllables, total number of sentences and total number of paragraphs were found through correlation analysis. Some regression equations with these factors as their variables were produced through regression analysis. A model estimating readability score from total number of unique syllables was a good formula, while a model with two factors, total number of unique syllables and new syllable occurrence ratio, was a better enhanced one. The readability score represents detailed level so we can recommend students read texts corresponding to their reading levels.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.255-260
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• 1997

일반적으로 한국어는 띄어쓰기 단위인 어절이 형태소 분석의 입력 단위로 쓰이고 있다. 그러나 실제 영역(real domain)에서 사용되는 텍스트에서는 띄어쓰기 오류와 같은 비문법적인 형태도 빈번히 쓰이고 있다. 따라서 형태소 분석 과정에 선행하여 적합한 형태소 분석의 단위를 인식하는 과정이 이루어져야 한다. 본 연구에서는 한국어의 음절 특성을 이용한 형태소분석을 위한 어절 인식 방법을 제안한다. 제안하는 방법은 사전에 기반하지 않고 원형코퍼스(raw corpus)로부터의 필요한 음절 정보 및 어휘정보를 추출하는 방법을 취하므로 오류가 포함된 문장에 대하여 견고한 분석이 가능하고 많은 시간과 노력이 요구되는 사전구축 및 관리 작업을 필요로 하지 않는다는 장점이 있다. 한국어 어절 인식을 위하여 본 논문에서는 세가지 확률 모텔과 동적 프로그래밍에 기반한 인식 알고리즘을 제안한다. 제안하는 모델들을 띄어쓰기 오류문제와 한국어 복합명사 분석 문제에 적용하여 실험한 결과 82-85%정도의 인식 정확도를 보였다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.33-36
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• 2004

최근 연속 음성 인식에서의 성능 향상을 위하여 음절을 인식 단위로 사용하고자 하는 노력들이 보고되고 있다. 그러나 음절의 경우 음소에 비해 학습성이 좋지 않고 모델의 수가 많으므로 음절 경계에서의 문맥 종속 모델링이 어렵다는 단점을 갖고 있다. 본 논문에서는 음절의 이러한 단점을 극복하기 위하여 모노폰과 트라이폰을 이용하여 음절 모델을 합성하는 방법을 제안한다. 제안된 모델은 트라이폰에 비하여 평균 $55\%$, PTM에 비하여 평균 $13\%$의 인식 속도 향상을 보이며, 동일한 속도일 경우 PTM, 트라이폰 모델 모두에 대하여 ERR이 약$8\%$ 향상됨을 볼 수 있었다.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.67-71
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• 2006

현재까지 제안된 자동 띄어쓰기 교정 모델들은 그 중의 대다수가 입력 문장에서 공백을 제거한 후에 교정 작업을 수행한다. 이러한 교정 방식은 입력 문장의 띄어쓰기가 잘 되어 있는 경우에 입력 문장보다 좋지 못한 교정 문장을 생성하는 경우가 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 입력 문장의 띄어쓰기를 고려한 자동 띄어쓰기 교정모델을 제안한다. 이 모델은 입력 문장의 음절단위 띄어쓰기 오류가 5%일 때 약 8%의 성능 향상을 보였으며, 10%의 오류가 존재할 때 약 5%의 성능 향상을 보였다.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.155-164
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• 2014

This paper proposes a new method to determine the recognition units for large vocabulary continuous speech recognition (LVCSR) in Korean by applying unsupervised segmentation and merging. In the proposed method, a text sentence is segmented into morphemes and position information is added to morphemes. Then submorpheme units are obtained by splitting the morpheme units through the maximization of posterior probability terms. The posterior probability terms are computed from the morpheme frequency distribution, the morpheme length distribution, and the morpheme frequency-of-frequency distribution. Finally, the recognition units are obtained by sequentially merging the submorpheme pair with the highest frequency. Computer experiments are conducted using a Korean LVCSR with a 100k word vocabulary and a trigram language model obtained by a 300 million eojeol (word phrase) corpus. The proposed method is shown to reduce the out-of-vocabulary rate to 1.8% and reduce the syllable error rate relatively by 14.0%.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.87-95
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• 2010

In this paper, we address issues associated with mixed-lingual text-to-speech based on context-dependent HMMs, where there are multiple sets of HMMs corresponding to each individual language. In particular, we propose smoothing techniques of synthesis parameters at the boundaries between different languages to obtain more natural quality of speech. In other words, mel-frequency cepstral coefficients (MFCCs) at the language boundaries are smoothed by applying several linear and nonlinear approximation techniques. It is shown from an informal listening test that synthesized speech smoothed by a modified version of linear least square approximation (MLLSA) and a quadratic interpolation (QI) method is preferred than that without using any smoothing technique.

• Journal of Korean language education
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• v.29 no.3
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• pp.25-53
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• 2018

This study examined the influence of lexical factors on verbal Eojeol recognition. To meet the goal, forty-five L2 Korean learners and twenty-two Korean native speakers took Eojeol decision tasks measured with the lexical factors such as 'number of strokes', 'number of consonants and vowels', 'number of syllables', 'number of morphemes', 'whole Eojeol frequency', 'root frequency', 'first-syllable-sharing frequency', and 'number of dictionary meanings.' As a result, 'whole Eojeol frequency' was the most effective factor to predict Eojeol recognition reaction time for native speakers and L2 learners, which supports the full-list model. Other lexical factors influencing Eojeol recognition reaction time in L2 learners were different following their proficiency level.