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dc.contributor.authorZhang, T.
dc.contributor.authorLee, Y. C.
dc.contributor.authorZhu, Y.
dc.contributor.authorHernando Pericás, Francisco Javier
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-10-29T19:19:02Z
dc.date.issued2018
dc.identifier.citationZhang, T., Lee, Y., Zhu, Y., Hernando, J. A conversation analysis framework using speech recognition and naïve bayes classification for construction process monitoring. A: Construction Research Congress. "Construction Research Congress 2018: Construction Information Technology: Selected papers from the Construction Research Congress 2018: April 2-4, 2018: New Orleans, Louisiana". American Society of Civil Engineers (ASCE), 2018, p. 572-580.
dc.identifier.isbn9780784481264
dc.identifier.urihttp://hdl.handle.net/2117/123196
dc.description.abstractAt a dynamic construction site, conversations convey vital information including construction activities, operation status, and task performance. Even though because of information security, recording the entire conversations of a construction project is currently somewhat restricted, establishing a framework to capture and analyze construction conversations would be a promising approach to enhance the utilization of new field information for construction progress monitoring and safety surveillance. The construction industry, however, has no proper method to deal with onsite conversations. To enhance construction process and safety monitoring that is crucial for construction project management, this paper proposes a new framework to acquire onsite conversations and analyze their significance and interrelationship. The proposed conversation analysis framework involves the integrated implementation of the speech recognition library and the Natural Language Processing Toolkit using the Naive Bayes classifier, which helps translate the conversations to a text script and classify them according to the distinct types of construction activities and operations. Using the conversation videos, this paper represents the translation and classification accuracy of construction relevant conversations. The web audio and text data related to three possible conversation topics at a construction site were collected and used to test the framework in this paper. The proposed framework reached 90.9% overall accuracy. This research is expected to help domain experts monitor construction work processes and make data-driven decisions based on analyzed onsite conversation data.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherAmerican Society of Civil Engineers (ASCE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject.lcshAutomatic speech recognition
dc.subject.otherCharacter recognition
dc.subject.otherClassifiers
dc.subject.otherConstruction
dc.subject.otherConstruction industry
dc.subject.otherExpert systems
dc.subject.otherNatural language processing systems
dc.subject.otherProcess monitoring
dc.subject.otherSecurity of data
dc.subject.otherSpeech recognition
dc.subject.otherBayes classification
dc.subject.otherClassification accuracy
dc.subject.otherConstruction activities
dc.subject.otherConstruction progress
dc.subject.otherConstruction project management
dc.subject.otherConstruction projects
dc.subject.otherConversation analysis
dc.subject.otherNaive Bayes classifiers
dc.subject.otherProject management
dc.titleA conversation analysis framework using speech recognition and naïve bayes classification for construction process monitoring
dc.typeConference report
dc.subject.lemacReconeixement automàtic de la parla
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.identifier.doi10.1061/9780784481264.056
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ascelibrary.org/doi/10.1061/9780784481264.056
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23395963
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorZhang, T., Lee, Y., Zhu, Y., Hernando, J.
upcommons.citation.contributorConstruction Research Congress
upcommons.citation.publishedtrue
upcommons.citation.publicationNameConstruction Research Congress 2018: Construction Information Technology: Selected papers from the Construction Research Congress 2018: April 2-4, 2018: New Orleans, Louisiana
upcommons.citation.startingPage572
upcommons.citation.endingPage580


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