• 대한음성학회지:말소리
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• 제56호
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• pp.207-223
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• 2005

The goal of language model adaptation is to improve the background language model with a relatively small adaptation corpus. This study presents a language model adaptation technique where additional text data for the adaptation do not exist. We propose the information retrieval (IR) technique with N-gram language modeling to collect the adaptation corpus from baseline text data. We also propose to use a dynamic language model interpolation coefficient to combine the background language model and the adapted language model. The interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech data reserved for held-out validation data. This allows the final adapted model to improve the performance of the background model consistently The proposed approach reduces the word error rate by $13.6\%$ relative to baseline 4-gram for two-hour broadcast news speech recognition.

• ETRI Journal
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• 제38권3호
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• pp.487-493
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• 2016

Two new methods are proposed for an unsupervised adaptation of a language model (LM) with a single sentence for automatic transcription tasks. At the training phase, training documents are clustered by a method known as Latent Dirichlet allocation (LDA), and then a domain-specific LM is trained for each cluster. At the test phase, an adapted LM is presented as a linear mixture of the now trained domain-specific LMs. Unlike previous adaptation methods, the proposed methods fully utilize a trained LDA model for the estimation of weight values, which are then to be assigned to the now trained domain-specific LMs; therefore, the clustering and weight-estimation algorithms of the trained LDA model are reliable. For the continuous speech recognition benchmark tests, the proposed methods outperform other unsupervised LM adaptation methods based on latent semantic analysis, non-negative matrix factorization, and LDA with n-gram counting.

• The Journal of the Acoustical Society of Korea
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• 제23권2E호
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• pp.51-55
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• 2004

In this paper, we propose a style-specific language model adaptation scheme using n-gram based tf*idf similarity for Korean spontaneous speech recognition. Korean spontaneous speech shows especially different style-specific characteristics such as filled pauses, word omission, and contraction, which are related to function words and depend on preceding or following words. To reflect these style-specific characteristics and overcome insufficient data for training language model, we estimate in-domain dependent n-gram model by relevance weighting of out-of-domain text data according to their n-. gram based tf*idf similarity, in which in-domain language model include disfluency model. Recognition results show that n-gram based tf*idf similarity weighting effectively reflects style difference.

• 영어어문교육
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• 제13권4호
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• pp.127-152
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• 2007

Recently, the number of students to choose to study in Australia has been increasing significantly. The purpose of this study is to examine how Korean primary school children perceive their own English language learning and cultural adaptation in Australia. A questionnaire survey was conducted with 34 Korean children aged 8-13 who were attending primary schools in Brisbane, Queensland. The study results show that they made diverse efforts to learn English language and culture in Australia, such as making English-speaking friends, watching TV/video/DVD, reading English books, and studying with a foreign tutor. Their English listening and writing abilities were thought to be improved most, followed by speaking, reading and cultural understanding after studying in Australia. The subjects were mostly satisfied with their study and life in Australia but they had difficulties with communicating in English, homesickness, foods, weather, insects, and discrimination. In particular, they had problems with understanding classes conducted all in English and participating in the classroom activities due to their low level of English ability and understanding of Australian classroom culture. The findings of this study have pedagogical implications for educators both in Australia and Korea.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.301-303
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• 2007

This paper presents user adaptation methods to overcome limitations of a user-independent model and a user-dependent model in a Korean sign language recognition system. To adapt model parameters for unobserved states in hidden Markov models, we introduce new methods based on motion similarity and prediction from adaptation history so that we can achieve faster adaption and higher recognition rates comparing with previous methods.

• 한국컴퓨터정보학회논문지
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• 제27권3호
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• pp.13-23
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• 2022

Prasad et al.는 사전학습(pre-trained)한 신경망 L1 글로다바(Gulordava) 언어모델을 여러 유형의 영어 관계절과 등위절 문장들로 적응 학습(adaptation learning)시켜 문장 간 유사성(sentence similarity)을 평가할 수 있는 통사 프라이밍(syntactic priming)-기반 프로빙 방법((probing method)을 제안했다. 본 논문에서는 한국인 영어학습자가 배우는 영어 자료를 바탕으로 훈련된 L2 LSTM 신경망 언어 모델의 영어 관계절 혹은 등위절 구조의 문장들에 대한 임베딩 표현 방식을 평가하기 위하여 프로빙 방법을 적용한다. 프로빙 실험은 사전 학습한 LSTM 언어 모델을 기반으로 추가로 적응 학습을 시킨 LSTM 언어 모델을 사용하여 문장 임베딩 벡터 표현의 통사적 속성을 추적한다. 이 프로빙 실험을 위한 데이터셋은 문장의 통사 구조를 생성하는 템플릿을 사용하여 자동으로 구축했다. 특히, 프로빙 과제별 문장의 통사적 속성을 분류하기 위해 통사 프라이밍을 이용한 언어 모델의 적응 효과(adaptation effect)를 측정했다. 영어 문장에 대한 언어 모델의 적응 효과와 통사적 속성 관계를 복합적으로 통계분석하기 위해 선형 혼합효과 모형(linear mixed-effects model) 분석을 수행했다. 제안한 L2 LSTM 언어 모델이 베이스라인 L1 글로다바 언어 모델과 비교했을 때, 프로빙 과제별 동일한 양상을 공유함을 확인했다. 또한 L2 LSTM 언어 모델은 다양한 관계절 혹은 등위절이 있는 문장들을 임베딩 표현할 때 관계절 혹은 등위절 세부 유형별로 통사적 속성에 따라 계층 구조로 구분하고 있음을 확인했다.

• 한국간호교육학회지
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• 제17권3호
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• pp.501-513
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• 2011

Purpose: On the basis of the theoretical framework of a combination of Roy's adaptation theory and Lazarus & Folkman's theory of stress - appraise coping, the purpose of this study was to predict effect factors of adaptation to university life of Chinese international students in Korea. After this, a model of adaptation to university life of Chinese international students in Korea was constructed. Methods: A questionnaire was used to survey 369 Chinese international students from one university in Korea, which was analyzed by using PASW Statistics 18.0 and LISREL 8.7. Results: This theoretical model explained adaptation to university life of Chinese international students at 75.0% in Korea. Physical symptoms, loneliness, acculturation stress and self-efficacy directly affected the adaptation to university life. Korean language proficiency indirectly affected adaptation to university life through self-efficacy. Conclusion: Results of this study provided theoretical basis for the future health care of university- centered health centers. For improving adaptation to university life of Chinese international students in Korea, education and nursing measures for reducing physical symptoms, loneliness and acculturation stress, and improving Korean language proficiency and self-efficacy are proposed for further research and development.

• The Journal of Korean Physical Therapy
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• 제26권5호
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• pp.344-350
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• 2014

Purpose: The purpose of this study was to translate and culturally adapt the International Classification of Functioning, Disability and Health (ICF) into the Korean language. Methods: The process of translation and adaptation of the ICF used here followed the translation guidelines of WHO. Implementation of this procedure comprised of four steps; forward translation, expert panel back-translation, pre-testing and cognitive interviewing, and final adaptation. The translators included health professionals with knowledge of ICF and non-health professionals blinded to the ICF. Clinical academics with significant experience in the use of disability survey, medical doctors, special educators, related policy makers, clinicians, architecture professionals, and international experts in ICF were invited to integrate all versions of the ICF for testing; 151 clinicians volunteered from 19 medical institutes across the country. Four different core-sets and a questionnaire were used for testing its practical usability and adaptation. Results: All translations were reviewed and a consensus was reached on any discrepancy from the earlier versions. Over 90% of the newly translated version of K-ICF was found to be different from the 2004 K-ICF version in the ICF language. Understanding of K-ICF language was responded difficult and very difficult by 50% of participants, whereas its practical use was responded 'useful' by more than 50% of subjects. Conclusion: It can be suggested that the new version of K-ICF should be widely used for final adaptation in the field of areas. Future studies will be required for implementation of K-ICF.

• The Journal of the Acoustical Society of Korea
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• 제16권1E호
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• pp.57-63
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• 1997

In this paper, we investigated performance of a vocabulary-independent speech recognizer with speaker adaptation. The vocabulary-independent speech recognizer does not require task-oriented speech databases to estimate HMM parameters, but adapts the parameters recursively by using input speech and recognition results. The recognizer has the advantage that it relieves efforts to record the speech databases and can be easily adapted to a new task and a new speaker with different recognition vocabulary without losing recognition accuracies. Experimental results showed that the vocabulary-independent speech recognizer with supervised offline speaker adaptation reduced 40% of recognition errors when 80 words from the same vocabulary as test data were used as adaptation data. The recognizer with unsupervised online speaker adaptation reduced abut 43% of recognition errors. This performance is comparable to that of a speaker-independent speech recognizer trained by a task-oriented speech database.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.83-86
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• 2003

This paper presents our style-based language model adaptation for Korean conversational speech recognition. Korean conversational speech is observed various characteristics of content and style such as filled pauses, word omission, and contraction as compared with the written text corpora. For style-based language model adaptation, we report two approaches. Our approaches focus on improving the estimation of domain-dependent n-gram models by relevance weighting out-of-domain text data, where style is represented by n-gram based tf*idf similarity. In addition to relevance weighting, we use disfluencies as predictor to the neighboring words. The best result reduces 6.5％ word error rate absolutely and shows that n-gram based relevance weighting reflects style difference greatly and disfluencies are good predictor.