A distributed platform for speech recognition research

Zhanibek Kozhirbayev, Shynggys Islam

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Distributed 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).
Original languageEnglish
Title of host publicationThe V International Scientifical and Practical Conference Informatization of Society
Place of PublicationAstana, Kazakhstan
Number of pages40
Publication statusPublished - Jun 2016


  • Distributed Computing, Sun GridEngine, Hadoop ecosystem, MapReduce

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