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Highly Scalable Speech Processing on Data Stream Management System

  • Shunsuke Nishii
  • Toyotaro Suzumura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)

Abstract

Today we require sophisticated speech processing technologies that process massive speech data simultaneously. In this paper we describe the implementation and evaluation of a Julius-backended parallel and scalable speech recognition system on the data stream management system “System S” developed by IBM Research. Our experimental result on our parallel and distributed environment with 4 nodes and 16 cores shows that the throughput can be significantly increased by a factor of 13.8 when compared with that on a single core. We also demonstrate that the beam management module in our system can keep throughput and recognition accuracy with varying input data rate.

Keywords

Speech Recognition Recognition Accuracy Beam Width Speech Data Word Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shunsuke Nishii
    • 1
  • Toyotaro Suzumura
    • 1
    • 2
  1. 1.Tokyo Institute of TechnologyTokyoJapan
  2. 2.IBM Research - TokyoKanagawaJapan

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