Title :
An improved consensus-like method for Minimum Bayes Risk decoding and lattice combination
Author :
Xu, Haihua ; Povey, Daniel ; Mangu, Lidia ; Zhu, Jie
Author_Institution :
Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
In this paper we describe a method for Minimum Bayes Risk decoding for speech recognition. This is a technique similar to Consensus a.k.a. Confusion Network Decoding, in which we attempt to find the hypothesis that minimizes the Bayes´ Risk with respect to the word error rate, based on a lattice of alternative outputs. Our method is an E-M like technique which makes approximations which we believe are less severe than the approximations made in Consensus, and our experimental results show an improvement in WER both for lattice rescoring and lattice-based system combination, versus baselines such as Consensus, Confusion Network Combination and ROVER.
Keywords :
Bayes methods; decoding; speech recognition; E-M like technique; WER; confusion network decoding; consensus-like method; lattice combination; lattice rescoring; lattice-based system combination; minimum Bayes risk decoding; speech recognition; word error rate; Decoding; Dynamic range; Equations; Error analysis; Lattices; Speech recognition; Confusion Network; Consensus; Lattice Rescoring; Minimum Bayes Risk (MBR); Speech Recognition;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495100
