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Development and evaluation of a spoken dialog system-mediated paired oral task for measuring second language oral communication ability in English
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In: Graduate Theses and Dissertations (2020)
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Multimodal continuous turn-taking prediction using multiscale RNNs ; ICMI 2018 - 20th ACM International Conference on Multimodal Interaction
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Προσωδιακή πραγμάτωση της πληροφοριακής δομής στα ελληνικά ... : Prosodic realization of information structure in greek ...
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Measuring the differences between human-human and human-machine dialogs
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In: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal; Vol. 4 No. 2 (2015); 99-112 ; ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal; Vol. 4 Núm. 2 (2015); 99-112 ; 2255-2863 ; 10.14201/ADCAIJ201542 (2015)
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Statistical Dialog Management for Health Interventions
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In: FIU Electronic Theses and Dissertations (2014)
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The User Model-Based Summarize and Re_ne Approach Improves Information Presentation in Spoken Dialog Systems
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: Elsevier, 2012
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On the Use of Machine Translation for Spoken Language Understanding Portability
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In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.inria.fr/inria-00523967 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Mar 2010, Dallas, Texas, United States. pp.5330 - 5333, ⟨10.1109/ICASSP.2010.5494960⟩ ; http://ieeexplore.ieee.org/iel5/5487364/5494886/05494960.pdf (2010)
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The User Model-Based Summarize and Re_ne Approach Improves Information Presentation in Spoken Dialog Systems
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In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.archives-ouvertes.fr/hal-00692184 ; Computer Speech and Language, Elsevier, 2010, 25 (2), pp.175. ⟨10.1016/j.csl.2010.04.003⟩ (2010)
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Error Awareness and Recovery in Conversational Spoken Language Interfaces
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In: DTIC (2007)
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Spoken Dialogue for Simulation Control and Conversational Tutoring
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In: DTIC (2004)
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Automated Tutoring Dialogues for Training in Shipboard Damage Control
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In: DTIC (2001)
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Detection and Correction of Repairs in Human-Computer Dialog
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In: DTIC (1992)
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Abstract:
The authors have analyzed 607 sentences of spontaneous human-computer speech data containing repairs that were drawn from a total corpus of 10,718 sentences. In this paper, they present criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics. In summary, disfluencies occur at high enough rates in human-computer dialog to merit consideration. In contrast to earlier approaches, the authors have made it their goal to detect and correct repairs automatically, without assuming an explicit edit signal. Without such an edit signal, however, repairs are easily confused both with false positives and with other repairs. Preliminary results show that pattern matching is effective at detecting repairs without excessive overgeneration. Their syntactic/semantic approaches are quite accurate at detecting repairs and correcting them. Acoustics is a third source of information that can be tapped to provide evidence about the existence of a repair. While none of these knowledge sources by itself is sufficient, they propose that by combining them, and possibly others, one can greatly enhance one's ability to detect and correct repairs. As a next step, they intend to explore additional aspects of the syntax and semantics of repairs, analyze further acoustic patterns, and pursue the question of how best to integrate information from these multiple knowledge sources. ; Sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation under grant NSF-IRI8905249. Technical Note No. 518. Pub. in the Proceedings of the Annual Meeting of the Association for Computational Linguistics under the title "Integrating Multiple Knowledge Sources for Detection and Correction of Repairs in Human-Computer Dialog," 1992.
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Keyword:
*AUTOMATION; *COMPUTATIONAL LINGUISTICS; *COMPUTERS; *CORRECTIONS; *DETECTION; *DISFLUENT SPEECH; *HUMAN-COMPUTER SPEECH; *HUMANS; *KNOWLEDGE INTEGRATION; *REPAIR; *SPEECH ANALYSIS; ACOUSTIC ANALYSIS; ACOUSTICS; AUTOMATIC CORRECTION; AUTOMATIC DETECTION; Cybernetics; DARPA SPOKEN LANGUAGE PROGRAM; DIGITIZED TRANSCRIPTIONS; DIGITIZED WAVEFORMS; DISFLUENCIES; ERRORS; Human Factors Engineering & Man Machine System; HUMAN-COMPUTER DIALOG; INFORMATION PROCESSING; Linguistics; MAN MACHINE SYSTEMS; MATCHING; NATURAL LANGUAGE; NATURAL LANGUAGE PROCESSING; PARSERS; PATTERN MATCHING; PATTERNS; SEMANTIC ANALYSIS; SEMANTICS; SPEECH REPAIR; SPONTANEOUS SPEECH DATA; SYMPOSIA; SYNTACTIC ANALYSIS; SYNTAX; Voice Communications
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URL: http://www.dtic.mil/docs/citations/ADA458689
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458689
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| 14 |
ON THE USE OF MACHINE TRANSLATION FOR SPOKEN LANGUAGE UNDERSTANDING PORTABILITY
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In: http://www-lium.univ-lemans.fr/%7Eservan/publications/Servan_ICASSP2010.pdf
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SPEECH RECOGNITION FOR A TRAVEL RESERVATION SYSTEM
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In: http://people.sabanciuniv.edu/~haerdogan/pubs/erdogan01srf.pdf
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