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Hits 101 – 120 of 200

101
Large Scale Language Independent Generation Using Thematic Hierarchies
In: DTIC (2001)
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102
Natural Language Generation in Dialog Systems
In: DTIC (2001)
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103
Research Into the Use of Speech Recognition Enhanced Microworlds in an Authorable Language Tutor
In: DTIC AND NTIS (1999)
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104
Improving Acoustic Models by Watching Television
In: DTIC AND NTIS (1998)
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105
Morphological Cues for Lexical Semantics
In: DTIC (1996)
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106
A Gentle Introduction to Soar, an Architecture for Human Cognition.
In: DTIC AND NTIS (1996)
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107
Evolving Fuzzy Logic Control Strategies using SAMUEL: An Initial Implementation.
In: DTIC AND NTIS (1996)
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108
Technology Review of the Use of Continuous Speech Recognition for Language Training.
In: DTIC AND NTIS (1996)
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109
A Self-Organizing Neural Network Architecture for Auditory and Speech Perception with Applications to Acoustic and other Temporal Prediction Problems.
In: DTIC AND NTIS (1995)
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110
The Unsupervised Acquisition of a Lexicon from Continuous Speech.
In: DTIC AND NTIS (1995)
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111
A Robust Loose Coupling for Speech Recognition and Natural Understanding.
In: DTIC AND NTIS (1995)
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112
Text-To-Speech Phrasing Enhancement System Using Neural Networks.
In: DTIC AND NTIS (1995)
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113
Robust Continuous Speech Recognition.
In: DTIC AND NTIS (1994)
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114
Mechanisms of implicit learning : connectionist models of sequence processing electronic resource
Cleeremans, Axel. - Cambridge, Massachusetts : MIT Press, 1993, [1993]©1993
MPI für Psycholinguistik
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115
UMass/Hughes: Description of the Circus System Used for MUC-5
In: DTIC (1993)
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116
A Comparison of Anapron with Seven other Name-Pronunciation Systems
In: DTIC AND NTIS (1993)
Abstract: This paper presents an experiment comparing a new name-pronunciation system, Anapron, with seven existing systems: three state-of-the-art commercial systems (from Bellcore Bell Labs, and Dec), two variants of a machine-learning system (NETtalk), and two humans. Anapron works by combining rule-based and case-based reasoning. It is based on the idea that it is much easier to improve a rule-based system by adding case-based reasoning to it than by tuning the rules to deal with every exception. In the experiment described here, Anapron used a set of rules adapted from MITalk and elementary foreign-language textbooks, and a case library of 5000 names. With these components -- which required relatively little knowledge engineering -- Anapron was found to perform almost at the level of the commercial systems, and significantly better than the two versions of NETtalk.
Keyword: *NAME PRONOUNCIATION; *PRONOUNCIATION; *SPEECH ARTICULATION; Cybernetics; ENGINEERING; FOREIGN LANGUAGES; HUMANS; LEARNING; LIBRARIES; Linguistics; MACHINES; REASONING; RULE BASED SYSTEMS; STATE OF THE ART; TEXTBOOKS
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA269988
http://www.dtic.mil/docs/citations/ADA269988
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117
Proceedings of the International Symposium on Methodologies for Intelligent Systems (Poster Session) (7th) held at Trondheim, Norway, on 15-18 June, 1993
In: DTIC AND NTIS (1993)
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118
Corpora and Data Preparation for Information Extraction
In: DTIC (1993)
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119
Tipster: Phase I
In: DTIC (1993)
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120
Human Learning of Schemas from Explanations in Practical Electronics
In: DTIC AND NTIS (1991)
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