文章基本信息

标题：Speeding up deep neural network based speech recognition systems
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作者：Xiao, Yeming
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：10
页码：2706-2712
DOI：10.4304/jsw.9.10.2706-2712
语种：English
出版社：Academy Publisher
摘要：Recently, deep neural network (DNN) based acousticmodeling has been successfully applied to largevocabulary continuous speech recognition (LVCSR) tasks.A relative word error reduction around 20% can beachieved compared to a state-of-the-art discriminativelytrained Gaussian Mixture Model (GMM). However, due tothe huge number of parameters in the DNN, real-time decodingis a bottleneck for the DNN based speech recognitionsystems. In this paper, we adopt several techniques for thespeed optimization of the DNN-based system. Specifically,we use singular value decomposition (SVD) to reduce themodel parameters, use the SSE instruction sets for theparallel calculation in the data space, and quantize themodel parameters reasonably to convert the floating-pointarithmetic into fixed-point arithmetic. Besides, taking thecharacteristics of speech signal into account, we use a frameskippingmethod when evaluating the posterior probabilities.Finally, compared to the un-optimized baseline system, withnegligible recognition performance loss, the decoding realtimefactor of the optimized one is significantly reduced,from 6.1 to 0.31. And this response speed can basically meetthe requirement of our real applications.
关键词：Large Vocabulary Continuous Speech Recognition;Acoustic Modeling;Deep Neural Network;SSE Instructions.