NDLI: A study of large vocabulary speech recognition decoding using finite-state graphs

Content Provider	IEEE Xplore Digital Library
Author	Zhijian Ou Ji Xiao
Copyright Year	2010
Description	Author affiliation: Department of Electronic Engineering, Tsinghua University, Beijing, China (Zhijian Ou; Ji Xiao)
Abstract	The use of weighted finite-state transducers (WFSTs) has become an attractive technique for building large vocabulary continuous speech recognition decoders. Conventionally, the compiled search network is represented as a standard WFST, which is then directly fed into a Viterbi decoder. In this work, we use the standard WFST representations and operations during compiling the search network. The compiled WFST is then equivalently converted to a new graphical representation, which we call finite-state graph (FSG). The resulting FSG is more tailored to Viterbi decoding for speech recognition and more compact in memory. This paper presents our effort to build a state-of-the-art WFST-based speech recognition system, which we call GrpDecoder. Benchmarking of GrpDecoder is carried out separately on two languages - English and Mandarin. The test results show that GrpDecoder which uses the new FSG representation in searching is superior to HTK's HDecode and IDIAP's Juicer for both languages, achieving lower error rates for a given recognition speed.
Starting Page	123
Ending Page	128
File Size	181954
Page Count	6
File Format	PDF
ISBN	9781424462445
e-ISBN	9781424462469
DOI	10.1109/ISCSLP.2010.5684837
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transducers Viterbi algorithm Memory management Hidden Markov models grpdecoder Speech recognition WFST Acoustics Decoding finite-state graph
Content Type	Text
Resource Type	Article

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