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A conversation analysis framework using speech recognition and naïve bayes classification for construction process monitoring
dc.contributor.author | Zhang, T. |
dc.contributor.author | Lee, Y. C. |
dc.contributor.author | Zhu, Y. |
dc.contributor.author | Hernando Pericás, Francisco Javier |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2018-10-29T19:19:02Z |
dc.date.issued | 2018 |
dc.identifier.citation | Zhang, 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.isbn | 9780784481264 |
dc.identifier.uri | http://hdl.handle.net/2117/123196 |
dc.description.abstract | At 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.extent | 9 p. |
dc.language.iso | eng |
dc.publisher | American Society of Civil Engineers (ASCE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic |
dc.subject.lcsh | Automatic speech recognition |
dc.subject.other | Character recognition |
dc.subject.other | Classifiers |
dc.subject.other | Construction |
dc.subject.other | Construction industry |
dc.subject.other | Expert systems |
dc.subject.other | Natural language processing systems |
dc.subject.other | Process monitoring |
dc.subject.other | Security of data |
dc.subject.other | Speech recognition |
dc.subject.other | Bayes classification |
dc.subject.other | Classification accuracy |
dc.subject.other | Construction activities |
dc.subject.other | Construction progress |
dc.subject.other | Construction project management |
dc.subject.other | Construction projects |
dc.subject.other | Conversation analysis |
dc.subject.other | Naive Bayes classifiers |
dc.subject.other | Project management |
dc.title | A conversation analysis framework using speech recognition and naïve bayes classification for construction process monitoring |
dc.type | Conference report |
dc.subject.lemac | Reconeixement automàtic de la parla |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1061/9780784481264.056 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ascelibrary.org/doi/10.1061/9780784481264.056 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 23395963 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Zhang, T.; Lee, Y.; Zhu, Y.; Hernando, J. |
local.citation.contributor | Construction Research Congress |
local.citation.publicationName | Construction Research Congress 2018: Construction Information Technology: Selected papers from the Construction Research Congress 2018: April 2-4, 2018: New Orleans, Louisiana |
local.citation.startingPage | 572 |
local.citation.endingPage | 580 |