Using Utterance and Semantic Level Confidence for Interactive Spoken Dialog Clarification
نویسندگان
چکیده
منابع مشابه
Using Utterance and Semantic Level Confidence for Interactive Spoken Dialog Clarification
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 c...
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Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between user's will and the system's understanding, and eventually result in a misinterpretation. To fill in the gap, people in human-to-human dialog try to clarify the major causes of the misunderstanding and selectively correct them....
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In the recent years, automated speech recognition has been the main drive behind the advent of spoken language interfaces, but at the same time a severe limiting factor in the development of these systems. We believe that increased robustness in the face of recognition errors can be achieved by making the systems aware of their own misunderstandings, and employing appropriate recovery technique...
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Recently, the performance of speech recognition was drastically improved, and the products with the interface based on speech recognition have been realized. However, when we communicate with computers through a speech interface, misrecognition is inevitable, and it is difficult to recover from it because of the immaturity of the interface. Users try to recover from misrecognition by a repetiti...
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Multi-domain spoken dialog system should be able to detect more than one domain from a user’s utterance. However, it is difficult to train an accurate binary classifier of a domain based on only positive and unlabeled examples. This paper improves hierarchical clustering algorithm to automatically identify reliable negative examples among unlabeled examples. This paper also verifies three linka...
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ژورنال
عنوان ژورنال: Journal of Computing Science and Engineering
سال: 2008
ISSN: 1976-4677
DOI: 10.5626/jcse.2008.2.1.001