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.
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Nishii, S., Suzumura, T. (2012). Highly Scalable Speech Processing on Data Stream Management System. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_14
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DOI: https://doi.org/10.1007/978-3-642-29035-0_14
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