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1
Authors' Responses - Regularities in motion: Apparent, real, and internalized
In: Behavioral and brain sciences. - New York, NY [u.a.] : Cambridge Univ. Press 24 (2001) 4, 757-761
OLC Linguistik
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2
Evolutionary internalized regularities
In: Behavioral and brain sciences. - New York, NY [u.a.] : Cambridge Univ. Press 24 (2001) 4, 626-628
OLC Linguistik
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3
Spatial cues in the cinematic discourse: Selection, function and style in Jurassic Park and Prospero's Books
In: Journal of pragmatics. - Amsterdam [u.a.] : Elsevier 26 (1996) 6, 767-792
OLC Linguistik
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4
A Hybrid Segmental Neural Net/Hidden Markov Model System for Continuous Speech Recognition
In: Institute of Electrical and Electronics Engineers. IEEE transactions on speech and audio processing. - New York, NY : Inst. 2 (1994) 1, 151-160
OLC Linguistik
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5
On Using Written Language Training Data for Spoken Language Modeling
In: DTIC (1994)
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6
Robust Continuous Speech Recognition.
In: DTIC AND NTIS (1994)
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7
BBN HARC and DELPHI Results on the ATIS Benchmarks - February 1991
In: DTIC (1991)
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8
Continuous Speech Recognition Using Segmental Neural Nets
In: DTIC (1991)
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9
Byblos Speech Recognition Benchmark Results
In: DTIC (1991)
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10
Improvements in the BYBLOS Continuous Speech Recognition System
In: DTIC AND NTIS (1990)
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11
Combining Multiple Knowledge Sources for Continuous Speech Recognition
In: DTIC AND NTIS (1989)
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12
Statistical Modeling for Continuous Speech Recognition
In: DTIC AND NTIS (1988)
Abstract: The authors' research into developing robust, high-performance continuous speech recognition systems for large-vocabulary tasks, such as battle management, has focused on the development of accurate mathematical models for the different phonemes that occur in English. The research performed in this project has been in three general areas: Hidden Markov Models, Stochastic Segment Models, and Rapid Speaker Adaptation. Hidden Markov models and stochastic segment models are two distinct methods of modeling phonetic coarticulation, i.e., the variation of phonemes in the context of other phonemes. The authors have tested the use of context-dependent hidden Markov models in BYBLOS, the BBN continuous speech recognition system, and report on word recognition accuracy in a 1000-word task domain. In contrast to hidden Markov modeling which models each part of a phoneme independently, stochastic segment modeling models each phoneme as a whole unit, and therefore has the promise of improved performance, as our preliminary experiments indicate.
Keyword: *MATHEMATICAL MODELS; *SPEECH RECOGNITION; ACCURACY; ADAPTATION; BATTLE MANAGEMENT; BATTLES; MANAGEMENT; MARKOV PROCESSES; MILITARY RESEARCH; PHONEMES; SPEECH; STATISTICAL ANALYSIS; STOCHASTIC PROCESSES; VARIATIONS; Voice Communications; WORD RECOGNITION
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA192054
http://www.dtic.mil/docs/citations/ADA192054
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13
Robust Coarticulatory Modeling for Continuous Speech Recognition.
In: DTIC AND NTIS (1986)
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14
Research in Continuous Speech Recognition
In: DTIC AND NTIS (1984)
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15
Linguistic innateness and its evidence
In: Language in primates (New York, 1983), p. 125-136
MPI für Psycholinguistik
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16
Talk to the animals
In: Language in primates (New York, 1983), p. 137-145
MPI für Psycholinguistik
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17
Research in Continuous Speech Recognition.
In: DTIC AND NTIS (1983)
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18
Imagery : there's more to it than meets the eye
In: Imagery (Cambridge, Mass., 1981), P.109-130
MPI für Psycholinguistik
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19
Speech Compression and Synthesis.
In: DTIC AND NTIS (1979)
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20
Speech Compression and Synthesis
In: DTIC AND NTIS (1979)
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