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tech,36-12-J05-1003,bq	ranking tasks </term> , for example , <term>	speech	recognition </term> , <term> machine translation	#8972 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example,speech recognition, machine translation, or natural language generation.
tech,16-2-C90-3014,bq	computational <term> phonological system </term> : <term>	speech	recognition </term> and <term> synthesis system	#16397 The approach of KPSG provides an explicit development model for constructing a computational phonological system:speech recognition and synthesis system.
other,10-2-N03-1012,bq	of <term> scoring </term> alternative <term>	speech	recognition hypotheses ( SRH ) </term> in	#2466 We apply our system to the task of scoring alternativespeech recognition hypotheses (SRH) in terms of their semantic coherence.
tech,5-1-C94-1030,bq	character recognition </term> and <term> continuous	speech	recognition </term> of a <term> natural language	#20619 In optical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted.
other,7-1-H90-1060,bq	contributions to <term> large vocabulary continuous	speech	recognition </term> . First , we present	#16985 This paper reports on two contributions to large vocabulary continuous speech recognition.
other,9-1-N03-2003,bq	language modeling </term> of <term> conversational	speech	</term> are limited . In this paper , we	#3024 Sources of training data suitable for language modeling of conversational speech are limited.
tool,16-2-H92-1074,bq	corpus </term> that has fueled <term> DARPA	speech	recognition technology </term> development	#19570 This corpus essentially supersedes the now old Resource Management (RM) corpus that has fueled DARPA speech recognition technology development for the past 5 years.
tool,11-1-H92-1074,bq	corpus </term> represents a new <term> DARPA	speech	recognition technology </term> development	#19538 The CSR (Connected Speech Recognition) corpus represents a new DARPA speech recognition technology development initiative to advance the state of the art in CSR.
other,26-1-N01-1003,bq	syntactic structure </term> for elementary <term>	speech	acts </term> and the decision of how to combine	#1319 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementaryspeech acts and the decision of how to combine them into one or more sentences.
other,26-2-H94-1034,bq	<term> likely repair </term> or as <term> fluent	speech	</term> . Other contextual clues , such as	#21332 The tagger is given knowledge about category transitions for speechrepairs, and so is able to mark a transition either as a likely repair or as fluent speech.
tech,5-1-C92-3165,bq	introduces a robust <term> interactive method for	speech	understanding </term> . The <term> generalized	#18146 This paper introduces a robust interactive method for speech understanding.
tech,17-3-H92-1036,bq	unified approach for the following four <term>	speech	recognition </term> applications , namely	#19116 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following fourspeech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training.
tech,4-2-H01-1055,bq	within reach . However , the improved <term>	speech	recognition </term> has brought to light	#934 However, the improvedspeech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user.
other,18-3-N03-1012,bq	semantically coherent and incoherent <term>	speech	recognition hypotheses </term> . An evaluation	#2497 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherentspeech recognition hypotheses.
lr,41-2-H90-1060,bq	traditional practice of using a little <term>	speech	</term> from many <term> speakers </term> . In	#17029 First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount of speech from a few speakers instead of the traditional practice of using a littlespeech from many speakers.
other,8-1-H01-1017,bq	users in robust , <term> mixed-initiative	speech	dialogue interactions </term> which reach	#215 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using.
other,8-1-C04-1103,bq	important role in many <term> multilingual	speech	and language applications </term> . In this	#5738 Machine transliteration/back-transliteration plays an important role in many multilingual speech and language applications.
tech,19-3-P03-1031,bq	due to the <term> ambiguity </term> of <term>	speech	understanding </term> , it is not appropriate	#4174 Since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity ofspeech understanding, it is not appropriate to decide on a single understandingresult after each user utterance.
lr,27-2-H90-1060,bq	</term> , which uses a large amount of <term>	speech	</term> from a few <term> speakers </term> instead	#17015 First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount ofspeech from a few speakers instead of the traditional practice of using a little speech from many speakers.
lr,23-6-H90-1060,bq	corpus </term> and a small amount of <term>	speech	</term> from the new ( target ) <term> speaker	#17142 Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount ofspeech from the new (target) speaker.


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