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Parallel scalability in speech recognition
Kisun You,Jike Chong,Youngmin Yi,Gonina, E.,Hughes, C.J.,Yen-Kuang Chen,Wonyong Sung,Keutzer, K. IEEE 2009 IEEE signal processing magazine Vol.26 No.6
<P>Parallel scalability allows an application to efficiently utilize an increasing number of processing elements. In this article, we explore a design space for parallel scalability for an inference engine in large vocabulary continuous speech recognition (LVCSR). Our implementation of the inference engine involves a parallel graph traversal through an irregular graph-based knowledge network with millions of states and arcs. The challenge is not only to define a software architecture that exposes sufficient fine-grained application concurrency but also to efficiently synchronize between an increasing number of concurrent tasks and to effectively utilize parallelism opportunities in today's highly parallel processors. We propose four application-level implementation alternatives called algorithm styles and construct highly optimized implementations on two parallel platforms: an Intel Core 17 multicore processor and a NVIDIA GTX280 manycore processor. The highest. performing algorithm style varies with the implementation platform. On a 44-mm speech data set, we demonstrate substantial speedups of 3.4 X on Core i7 and 10.5 X on GTX280 compared to a highly optimized sequential implementation on Core 17 without sacrificing accuracy. The parallel implementations contain less than 2.5% sequential overhead, promising scalability and significant potential for further speedup on future platforms.</P>
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