A distributed platform for speech recognition research

dc.contributor.authorKozhirbayev, Zhanibek
dc.contributor.authorIslam, Shynggys
dc.date.accessioned2018-05-28T09:56:24Z
dc.date.available2018-05-28T09:56:24Z
dc.date.issued2016-06-17
dc.description.abstractDistributed and parallel processing of big data has been applied in various applications for the past few years. Moreover, huge advancements took place in usability, economic efficiency, and multiplicity of parallel processing systems, with big data analysis and speech recognition research supported by many researchers. In this paper we examined and investigated which parts of speech recognition research may be parallelized and computed using distributed computing platforms. Firstly, we address the case of efficiently computing n-gram statistics on MapReduce platforms to build a language model (LM). Secondly, we show how the Automated Speech Recognition (ASR) tool can work efficiently regarding the speed and fault-tolerance in distributed environment such as Sun GridEngine (SGE).en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/3210
dc.language.isoenen_US
dc.publisherNational Laboratory Astanaen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectMapReduceen_US
dc.subjectHadoop ecosystemen_US
dc.subjectSun GridEngineen_US
dc.subjectDistributed Computingen_US
dc.titleA distributed platform for speech recognition researchen_US
dc.typeConference Paperen_US
workflow.import.sourcescience

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