• 대한음성학회지:말소리
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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.

• Journal of Computing Science and Engineering
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• 제4권1호
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• pp.1-22
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• 2010

A field of spoken dialog systems is a rapidly growing research area because the performance improvement of speech technologies motivates the possibility of building systems that a human can easily operate in order to access useful information via spoken languages. Among the components in a spoken dialog system, the dialog management plays major roles such as discourse analysis, database access, error handling, and system action prediction. This survey covers design issues and recent approaches to the dialog management techniques for modeling the dialogs. We also explain the user simulation techniques for automatic evaluation of spoken dialog systems.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.311-314
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognizer, 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 editting corpus and training each models, we need more systematic and efficent working environment. For the working environment, we propose DialogStudio as an spoken dialog system workbench.

• Journal of Computing Science and Engineering
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• 제2권1호
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• pp.1-25
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• 2008

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between the user's intention and the system's understanding, which eventually results in a misinterpretation. To fill in the gap, people in human-to-human dialogs try to clarify the major causes of the misunderstanding to selectively correct them. This paper presents a method of clarification techniques to human-to-machine spoken dialog systems. We viewed the clarification dialog as a two-step problem-Belief confirmation and Clarification strategy establishment. To confirm the belief, we organized the clarification process into three systematic phases. In the belief confirmation phase, we consider the overall dialog system's processes including speech recognition, language understanding and semantic slot and value pairs for clarification dialog management. A clarification expert is developed for establishing clarification dialog strategy. In addition, we proposed a new design of plugging clarification dialog module in a given expert based dialog system. The experiment results demonstrate that the error verifiers effectively catch the word and utterance-level semantic errors and the clarification experts actually increase the dialog success rate and the dialog efficiency.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.32-35
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• 2003

This paper describes our recent work for developing a baseline platform for Korean spoken dialog recognition. In our work, We have collected about 65 hour speech corpus with auditory transcriptions. Linguistic information on various levels such as mophology, syntax, semantics, and discourse is attached to the speech database by using automatic or semi-automatic tools for tagging linguistic information.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2008년도 제20회 한글 및 한국어 정보처리 학술대회
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• pp.156-161
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• 2008

This work presents an agenda-based approach to improve the robustness of the dialog manager by using dialog examples and n-best recognition hypotheses. This approach supports n-best hypotheses in the dialog manager and keeps track of the dialog state using a discourse interpretation algorithm with the agenda graph and focus stack. Given the agenda graph and n-best hypotheses, the system can predict the next system actions to maximize multi-level score functions. To evaluate the proposed method, a spoken dialog system for a building guidance robot was developed. Preliminary evaluation shows this approach would be effective to improve the robustness of example-based dialog modeling.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권2호
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• pp.129-136
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• 2010

본 논문에서는 영어 회화 교육을 위한 예제 기반 대화 시스템에 대해 논한다. 기존의 획일적인 멀티미디어 영어 학습에서 벗어나 자연어 처리 및 대화 기술을 이용하여 지능적인 일대일 영어 회화 교육 제공을 목적으로 한다. 본 시스템은 미숙한 학습자 발화를 이해할 수 있으므로 불완전한 언어 구사 능력으로도 대화를 참여할 수 있는 체험형 학습을 제공한다. 이를 통해 학습자에게 영어를 배우려는 흥미로운 동기를 부여한다. 또한 학습자의 표현력 향상을 위한 교육적인 도움 기능을 갖추고 있다. 이를 위해 우리는 학습자의 미숙한 표현을 이해하는 담화 상황 고려 발화의도 인식 모델, 도메인 확장성이 뛰어난 예제 기반 대화 관리 모델, 교육 및 평가 기능을 개발하였다. 실험 결과 학습자의 발화에 에러가 많아도 높은 발화의도 인식 성능을 보였으며 대화 상황에 적합한 피드백을 제공하여 학습자가 회화 연습을 끝까지 마치도록 도와 교육 효과에 이바지함을 알 수 있었다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2012년도 제24회 한글 및 한국어 정보처리 학술대회
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• pp.215-218
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• 2012

본 논문에서는 한국어 대화 처리를 위한 통계기반 음성언어이해 시스템에 대해 기술한다. 음성언어이해시스템은 대화처리에서 음성 인식된 문장으로부터 사용자의 의도를 인식하여 의미표현으로 표현하는 기능을 담당한다. 한국어의 특성을 반영한 실용적인 음성언어이해 시스템을 위해서 강건성과 적용성, 확장성 등이 요구된다. 이를 위해 본 시스템은 음성언어의 특성상 구조분석을 하지 않고, 마이닝 기법을 이용하여 사용자 의도 표현을 생성하는 방식을 취하고 있다. 또한 한국어에서 나타나는 특징들에 대한 처리를 위해 자질 추가 및 점규화 처리 등을 수행하였다. 정보서비스용 대화처리 시스템을 대상으로 개발되고 있고, 차량 정보서비스용 학습 코퍼스를 대상으로 실험을 하여 문장단위 정확률로 약 89%의 성능을 보이고 있다.

• 대한음성학회지:말소리
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• 제45호
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• pp.15-22
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• 2003

This study is a kind of a preliminary study to develop naturalness of dialog TTS system. In this study, as major characteristics of politeness in Korean, temporal(total duration of utterances, speech rate and duration of utterance final syllables) and F0(mean F0, boundary tone pattern, F0 range) features were discussed through acoustic analysis of recorded data of semantically neutral sentences, which were spoken by ten professional voice actors under two conditions of utterance type - namely, normal and polite type. The results show that temporal characteristics were significantly different according to the utterance type but F0 characteristics were not.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.719-722
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• 2009

The purpose of this paper is to develop a robot that moves independently, communicates with a human, and explicitly extracts information from the human mind that is rarely expressed verbally. In a spoken dialog system for information collection, it is desirable to continue communicating with the user as long as possible, but not if the user does not wish to communicate. Therefore, the system should be able to terminate the communication before the user starts to object to using it. In this paper, to enable the construction of a decision model for a system to decide when to stop communicating with a human, we acquired speech and motion data from individuals who were asked many questions by another person. We then analyze their speech and body motion when they do not mind answering the questions, and also when they wish the questioning to cease. From the results, we can identify differences in speech power, length of pauses, speech rate, and body motion.