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1
Study of Discussion Record Analysis Using Temporal Data Crystallization and Its Application to TV Scene Analysis
In: DTIC (2015)
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2
Understanding Tonal Languages
In: DTIC (2013)
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3
Computational Modeling of Emotions and Affect in Social-Cultural Interaction
In: DTIC (2013)
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4
A Submodularity Framework for Data Subset Selection
In: DTIC (2013)
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5
Speech Synthesis Using Perceptually Motivated Features
In: DTIC (2012)
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6
Machine Recognition vs Human Recognition of Voices
In: DTIC (2012)
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7
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
In: DTIC (2012)
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8
USSS-MITLL 2010 Human Assisted Speaker Recognition
In: DTIC (2010)
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9
Multitalker Speech Perception with Ideal Time-Frequency Segregation: Effects of Voice Characteristics and Number of Talkers
In: DTIC (2009)
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10
Pictures from Words, Pictures from Text: Constructing Pictorial Representations of Meaning from Text
In: DTIC (2009)
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11
The Multi-Session Audio Research Project (MARP) Corpus: Goals, Design and Initial Findings
In: DTIC (2009)
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12
Iterated Class-Specific Subspaces for Speaker-Dependent Phoneme Classification
In: DTIC (2008)
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13
Multimodal Meeting Capture and Understanding with the CALO Meeting Assistant
In: DTIC (2007)
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14
Error Awareness and Recovery in Conversational Spoken Language Interfaces
In: DTIC (2007)
Abstract: One of the most important and persistent problems in the development of conversational spoken language interfaces is their lack of robustness when confronted with understanding-errors. Most of these errors stem from limitations in current speech recognition technology, and, as a result, appear across all domains and interaction types. There are two approaches towards increased robustness: prevent the errors from happening, or recover from them through conversation, by interacting with the users. In this dissertation we have engaged in a research program centered on the second approach. We argue that three capabilities are needed in order to seamlessly and efficiently recover from errors: (1) systems must be able to detect the errors, preferably as soon as they happen, (2) systems must be equipped with a rich repertoire of error recovery strategies that can be used to set the conversation back on track, and (3) systems must know how to choose optimally between different recovery strategies at run-time, i.e. they must have good error recovery policies. This work makes a number of contributions in each of these areas.
Keyword: *ERRORS; *MAN COMPUTER INTERFACE; *POLICIES; *RAVENCLAW FRAMEWORK; *RECOVERY; *RECOVERY STRATEGIES; *SPEECH RECOGNITION; *SPOKEN DIALOG SYSTEMS; ARCHITECTURE; CONVERSATIONS; Cybernetics; DETECTION; ERROR HANDLING; LEARNING MACHINES; MISUNDERSTANDINGS; NON-UNDERSTANDINGS; SPOKEN LANGUAGE INTERFACES; THESES; Voice Communications
URL: http://www.dtic.mil/docs/citations/ADA476845
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA476845
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15
Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings
In: DTIC (2006)
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16
Investigation on Mandarin Broadcast News Speech Recognition
In: DTIC (2006)
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17
Thai Automatic Speech Recognition
In: DTIC (2005)
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18
Objective Measurement of the Speech Transmission Quality of Vocoders by Means of the Speech Transmission Index
In: DTIC (2005)
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19
Cognitive Tools for Humanoid Robots in Space
In: DTIC AND NTIS (2004)
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20
Towards a Warfighter's Associate: Eliminating the Operator Control Unit
In: DTIC (2004)
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