The Experts below are selected from a list of 10539 Experts worldwide ranked by ideXlab platform
Gary Geunbae Lee - One of the best experts on this subject based on the ideXlab platform.
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ASRU - Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.
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Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2007 Proceedings, 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system. ©2007 IEEE.
Chul Lee - One of the best experts on this subject based on the ideXlab platform.
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ASRU - Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.
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Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2007 Proceedings, 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system. ©2007 IEEE.
Donghyeon Lee - One of the best experts on this subject based on the ideXlab platform.
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ASRU - Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.
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Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2007 Proceedings, 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system. ©2007 IEEE.
S Jung - One of the best experts on this subject based on the ideXlab platform.
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ASRU - Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.
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Example-based error Recovery Strategy for spoken dialog system
2007 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2007 Proceedings, 2007Co-Authors: Chul Lee, Donghyeon Lee, S Jung, Gary Geunbae LeeAbstract:Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error Recovery is focused on the re-phrase Strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system. ©2007 IEEE.
Benoit Favre - One of the best experts on this subject based on the ideXlab platform.
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ASR error segment localization for spoken Recovery Strategy
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2013Co-Authors: Frédéric Béchet, Benoit FavreAbstract:Even though small ASR errors might not impact downstream processes that make use of the transcript, larger error segments like those generated by OOVs can have a considerable impact on applications such as speech-to-speech translation and can eventually lead to communication failure between users of the system. This work focuses on error detection in ASR output targeted towards significant error segments that can be recovered using a dialog system. We propose a CRF system trained to recognize error segments with ASR confidence-based, lexical and syntactic features. The most significant error segment is passed to a dialog system for interactive Recovery in which rephrased words are reinserted in the original. 22% of utterances can be fully recovered and an interesting by-product is that rewriting error segments as a single token reduces WER by 17% on an adverse corpus. © 2013 IEEE.