• Proceedings of the KSPS conference
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• 2002.11a
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• pp.197-200
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• 2002

In this paper, we propose a phoneme duration modeling in a speech recognition system based on disicion tree state tying. We assume that phone duration has a Gamma distribution. In a training mode, we model mean and variance of each state duration in context-independent phone model based on decision tree state tying. In a recognition mode, we get mean and variance of each context-dependent phone duration form state duration information obtaind during training mode. We make a comparative study of the proposed meth with conventinal methods. Our method results in good performance compared with conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4E
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• pp.11-16
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• 1998

For large vocabulary speech recognition using HMMs, context-dependent subword units have been often employed. However, when context-dependent phone models are used, they result in a system which has too may parameters to train. The problem of too many parameters and too little training data is absolutely crucial in the design of a statistical speech recognizer. Furthermore, when building large vocabulary speech recognition systems, unseen triphone problem is unavoidable. In this paper, we propose the modified phonetic decision tree algorithm for the automatic prediction of unseen triphones which has advantages solving these problems through following two experiments in Japanese contexts. The baseline experimental results show that the modified tree based clustering algorithm is effective for clustering and reducing the number of states without any degradation in performance. The task experimental results show that our proposed algorithm also has the advantage of providing a automatic prediction of unseen triphones.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• MALSORI
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• no.45
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.591-596
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• 2007

본 연구에서는 상황 추론의 기능을 추천 시스템에 접목하였다. 연구의 대상 영역은 음악 추천 분야인데, 본 연구에서 제안하는 시스템은 세 개의 모듈, 즉 Intention Module, Mood Module 그리고 Recommendation Module로 구성되어 있다. Intention Module은 사용자가 음악을 청취할 의향이 있는지 없는지를 외부 환경의 상황 데이터를 이용하여 추론한다. Mood Module은 사용자의 상황에 적합한 음악의 장르를 추론한다. 마지막으로 Recommendation Module은 사용자에게 선정된 장르의 음악을 추천한다.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.74-88
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• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.147-160
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• 2008

BPMS(business process management system) facilitates defining new processes or updating existing processes. However, processing of exceptional or nonroutine task requires the intervention of domain experts or introduction of the situation specific resolution process. This paper assumes sufficient amount of business process exception handling cases are stored in the process repository. Since the retrieval of the best exception handling process requires a good understanding about the exceptional situation, context awareness is an important issue. To facilitate the understanding of exceptional situation and to enable the efficient selection of the best exception handling process, we adopted the 'situation variable' and 'decision variable' construct. A case example for exception handling in the e-commerce delivery process is provided to illustrate how the proposed construct works. Application of the C5.0 algorithm guarantees the construction of an optimum search tree. It also implies that an efficient search path has been identified for the context aware selection of the best exception handling process.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.182-188
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• 2007

모바일 음악 재생 장치의 보급이 활성화 되면서 모바일 환경에서 음악을 즐기고자 하는 욕구가 확대되었다. 모바일 음악 재생 장치의 저장 공간이 확대되어 한번에 많은 곡을 보유하게 되었지만, 실제적으로 많은 곡 중에서 자신이 원하는 곡을 쉽게 찾을 수 있는 방법이 존재하지 않고 있다. 이런 문제들을 해결하기 위해 해당 음악 고유의 Metadata를 활용하여 쉽게 찾고자 하는 경향이 있지만, 현재까지는 단순한 사용자 인터페이스만 제공하고 있다. 각각의 음악은 음악 고유의 Context도 가지게 되며, 사용자의 음악 감상 로그에 의해 생성되는 개인화 Context 및 같은 음악을 즐기는 다른 사용자들에 의해 생성되는 Social Context등으로 하나의 음악과 관련된 Context가 증가되는 추세에 있다. 이와 같이 추가되는 Context들은 음악을 탐색하는데 있어 효과적인 수단이 제공되어야 하지만 모바일 음악 재생장치에 있는 음악 탐색 프로그램은 새롭게 추가되는 Context 에 의한 효과적으로 대응하지 못하고 있다. 이러한 문제점을 해결하기 위해서 모바일 장치에 Embedded Database Engine을 장착하여 동적 트리 생성/탐색을 통한 Context 기반 연관 음악 탐색 방법을 제안하게 되었다. 본 논문에서는 새롭게 고안된 음악 탐색 방법에 대하여 3가지 사항에 대해서 제시하였다. 첫째, 새로운 Context추가에 대한 동적 메뉴 추가 방법을 제시하였다. 둘째, 실제로 새롭게 추가된 사용자 인터페이스에 대해서 알아보았으며, 마지막으로 제시된 방법이 얼마나 효과적인가를 계산하여 일반화 하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.8-13
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• 2007

The context-aware environment focuses on recognizing the context and physical entities. For this reason, there has been an increasement in research of context-aware computing environment. Ontology-based context models are widely used in ubiquitous environment because of context sharing and reusing. In this paper, we propose a proactive retrieval method using ontology in context-aware environment. The method use a concept level of hierarchical concept tree in ontology for more efficient retrieval. This paper describes the proactive retrieval method and ontology model. Several experiments are performed and the results verify that the proposed method's efficiency is better than other existing methods.