W13-3915 discussed in previous studies of spontaneous speech recognition -LSB- 5 , 6 -RSB- . Spontaneous
P99-1002 is also an important problem in spontaneous speech recognition . In typical laboratory experiments
P06-1093 there are other issues related to spontaneous speech recognition in the transcriptions . There
P99-1002 perspectives of linguistic modeling for spontaneous speech recognition / understanding . Section 6 concludes
W09-3410 POS-tagged corpora that are used for spontaneous speech recognition in S2ST system . While referring
W06-3711 . We are working on improving spontaneous speech recognition accuracy and more naturally integrating
W02-0718 order to afford the problem of spontaneous speech recognition , there are proposals -LSB- 14
P99-1002 MODELING 5.1 Language modeling for spontaneous speech recognition One of the most important issues
H91-1022 capable of handling continuous and spontaneous speech recognition as well as natural language understanding
W04-3013 recognition rates fall below 35 % for spontaneous speech recognition ( Eagle & Pentland , 2002
H92-1004 tabulation of the February 1992 ATIS spontaneous Speech RECognition ( SPREC ) test results . Results
H94-1011 in prior years , tests included spontaneous speech recognition ( SPREC ) tests , natural language
P08-2027 frequency of this phenomenon , spontaneous speech recognition systems will need to be able
H92-1004 Tests include : ( 1 ) ATIS domain spontaneous speech recognition system tests , ( 2 ) ATIS natural
H92-1068 In this experiment , we compare spontaneous speech recognition performance given different combinations
H92-1012 use in the common evaluation of spontaneous speech recognition and understanding of text and
H91-1008 speech ) RM1 test material . ATIS Spontaneous Speech Recognition Component Tests ( SPREC ) Table
H92-1107 . 2 . RECENT RESULTS * Reduced spontaneous speech recognition word error rate by more than
H01-1059 dialog task , and the ARPA 1994 Spontaneous Speech Recognition ( SPREC ) ATIS-3 data ( ATIS94
W09-3410 Conversational and Their Evaluations on Spontaneous Speech Recognitions </title> Xinhui Hu Ryosuke Isotani
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