C02-2014 seems promising for continuous recognition systems . Its main advantage is the ease
C00-2158 reduce the error rate of speech recognition systems used by professional translators
A00-2014 compared to most current speech recognition systems . The CDG parser parses the word
C82-1026 I might cause quite different recognition systems for the same task . The main
C02-2014 to be integrated with existing recognition systems . Conclusion This paper has introduced
A00-1012 transcriptions produced by automatic speech recognition systems . An experiment which determines
C88-1035 important problem in all speech recognition systems is the inherent uncertainty associated
C73-2031 account when constructing automatic recognition systems . The position words in German
C00-2158 using current offthe-shelf speech recognition systems is that these systems have high
C92-4199 rite others are specific to name recognition systems only . 1 . Two-character names
C88-1071 English . 1 Introduction Speech recognition systems consist of two components . An
A00-1040 state-of-the-art named entity recognition systems . However , we then show that
C88-1071 NATURAL LANGUAGE Abstract Speech recognition systems incorporate a language model
C80-1070 programming . i. Introduction Speech recognition systems can be classified according to
C02-2014 Recognition Assistant supports existing recognition systems rather than replacing them .
A83-1030 , and modestly priced , speech recognition systems are on the market and in use
C04-1110 Difficulties arise because speech recognition systems are not perfect . Therefore ,
C02-2014 developed to communicate with existing recognition systems . An example for the dialogue
C82-1026 cases . That means , an ` optimal recognition systems ' can not be defined without
C02-2003 commercially available speech recognition systems , we found that for a vocabulary
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