C02-1136 number of studies have been made on spoken dialogue systems . For the purpose of smooth interaction
A92-1040 addresses two problems facing spoken dialogue systems . The first is that , for maximum
C02-1152 past years , a great number of spoken dialogue systems have been developed . Their modelspeechunderstandingprocessasconvert
C04-1158 confirmation is indispensable in spoken dialogue systems to eliminate the misunderstandings
A00-2028 many re - spects . We show how spoken dialogue systems can learn to support more natural
A00-2027 distinguish MIMIC from other existing spoken dialogue systems . First , MIMIC automatically
A00-2029 dialogue manager in most current spoken dialogue systems ( SDSs ) is error handling .
C04-1043 therefore has the potential to advance spoken dialogue systems . 2 Background Categorial Grammars
C00-1068 robustness is one of the reason why spoken dialogue systems have not been widely deployed
C04-1158 for keywords is indispensable in spoken dialogue systems to eliminate misunderstandings
C00-1073 language , and expertise with spoken dialogue systems . During both training and testing
D13-1013 . Such a parser is useful for spoken dialogue systems which typically encounter disfluent
A00-2027 speech technologies have enabled spoken dialogue systems to employ mixed initiative dialogue
A00-1014 of experimental prototypes of spoken dialogue systems . Acknowledgments The author
A00-1014 general framework for developing spoken dialogue systems with different adaptation behavior
A00-2028 Diane Litman Abstract Current spoken dialogue systems are deficient in their strategies
D15-1027 Henderson Abstract When building spoken dialogue systems for a new domain , a major bottleneck
A00-2028 experience . One way that current spoken dialogue systems are quite limited is in their
A00-2028 the baseline . 1 Introduction Spoken dialogue systems promise efficient and natural
D11-1054 been investigated in the area of spoken dialogue systems ( Jung et al. , 2009 ) , it is
hide detail