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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.
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,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,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.
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.
tech,15-3-P03-1030,bq	enhancing techniques including <term> part of	speech	tagging </term> , new <term> similarity measures	#4109 Motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists.
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.
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.
other,12-3-I05-5003,bq	<term> PER </term> which leverages <term> part of	speech	information </term> of the <term> words </term>	#8381 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence.
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.
other,70-5-E06-1035,bq	<term> cue phrases </term> and <term> overlapping	speech	</term> , are better indicators for the top-level	#10597 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.
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,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.
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.
other,35-3-H92-1003,bq	<term> utterances </term> of <term> spontaneous	speech	</term> from five sites for use in a <term>	#18598 We summarize the motivation for this effort, the goals, the implementation of a multi-site data collection paradigm, and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech, natural language and spoken language
other,15-1-H92-1010,bq	</term><term> LIMSI </term> in the field of <term>	speech	processing </term> , but also in the related	#18632 The paper provides an overview of the research conducted at LIMSI in the field ofspeech processing, but also in the related areas of Human-Machine Communication, including Natural Language Processing, Non Verbal and Multimodal Communication.
measure(ment),16-3-H92-1016,bq	modifications combined to reduce the <term>	speech	recognition word and sentence error rates	#18753 Together with the use of a larger training set, these modifications combined to reduce thespeech recognition word and sentence error rates by a factor of 2.5 and 1.6, respectively, on the October '91 test set.
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.
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.
tech,28-1-H92-1095,bq	<term> language understanding </term> with <term>	speech	recognition </term> , <term> knowledge-based	#19665 Language understanding work at Paramax focuses on applying general-purpose language understanding technology to spoken language understanding, text understanding, and document processing, integrating language understanding withspeech recognition, knowledge-based information retrieval and image understanding.


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