dc.creatorSigcha, E
dc.creatorEspinoza Mejía, Jorge Mauricio
dc.creatorMedina, J
dc.creatorSaquicela Galarza, Víctor Hugo
dc.creatorVega, F
dc.date2018-01-11T16:47:50Z
dc.date2018-01-11T16:47:50Z
dc.date2017-09-19
dc.dateinfo:eu-repo/date/embargoEnd/2022-01-01 0:00
dc.date.accessioned2018-03-14T20:32:44Z
dc.date.available2018-03-14T20:32:44Z
dc.identifier9783319665610
dc.identifier18650929
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85028800153&doi=10.1007%2f978-3-319-66562-7_49&partnerID=40&md5=fc942b108a228279f3e96b2b1984f4d1
dc.identifierhttp://dspace.ucuenca.edu.ec/handle/123456789/29245
dc.identifier10.1007/978-3-319-66562-7_49
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1136147
dc.descriptionA key element to enable the analysis and accessing to radio broadcast content is the development of automatic speech-to-text systems. The building of these systems has been possible given the current available of different speech resources, models, and open source services designed mainly for English language. However, the most of these tools have been migrated to other languages like Spanish for avoiding the creation of these systems from scratch. Despite existing efforts there is no clear evidence of the tools that can be used to convert audio to text in other dialects of Spanish. Also, the most of these systems are trained to consider a specific context, therefore, audio transcription systems personalized for a language and a specific context are needed. This article describes the implementation of an architecture oriented to automatic speech-to-text transcription applied on Ecuadorian radio broadcasters, using available free tools for performing audio segmentation and transcription. The selected tools were evaluated measuring their performance and facilities for adjusting to the defined architecture. At the end, a Web application was developed and its final performance was compared with IBM Watson speech to text service; the results show that the proposed system improves the accuracy and achieves a Word Error Rate around 10%. The obtained results allow to suggest the use of a free tools set in order to train models oriented to specific speech-to-text transcription scenarios.
dc.descriptionCali
dc.languageen_US
dc.publisherSPRINGER VERLAG
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/3.0/ec/
dc.sourceinstname:Universidad de Cuenca
dc.sourcereponame:Repositorio Digital de la Universidad de Cuenca
dc.sourceCommunications in Computer and Information Science
dc.subjectAudio content analysis
dc.subjectAutomatic audio segmentation
dc.subjectAutomatic speech recognition
dc.subjectPython
dc.subjectSpeech to text
dc.titleAutomatic speech-to-text transcription in an ecuadorian radio broadcast context
dc.typeArtículos de revistas


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