NDLI: A Memory-Efficient Graph Structured Composite-State Network for Embedded Speech Recognition

Content Provider	IEEE Xplore Digital Library
Author	Jianguang Weng Xiaowen Jia
Copyright Year	2009
Abstract	There is a great demand to optimize the search engine of embedded speech recognition (ESR) system to make it applicable for low-resource portable devices. This paper focuses on the construction of a memory-efficient search space representation. To reduce the number of HMM models in prefix part, tree structured phonetic network is generally used. However, it still suffers from several kinds of redundancy, such as redundancy in suffix part, in state level and in topological structure. In order to eliminate such redundancy, we present a novel graph structured composite-state network. A comparison with traditional tree structured phonetic network shows that, our proposed network can reduce the memory footprint by a compression factor of 1.5 even with a relative speed up of 15.5% and without any loss in recognition accuracy.
Starting Page	570
Ending Page	573
File Size	895183
Page Count	4
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Embedded computing Portable computers Paramagnetic resonance Target recognition HMM Tree graphs speech recognition Hidden Markov models Speech recognition Search engines Computer networks Decision trees composite-state network
Content Type	Text
Resource Type	Article

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