Paper Info
Title
Factor analysis based VTS and JUD noise estimation and compensation
Abstract
Model based compensation schemes are a powerful approach for noise robust speech recognition. Recently there have been a number of investigations into adaptive training, and estimating the noise models used for model adaptation. This paper examines the use of EM-based schemes for both canonical models and noise estimation, including discriminative adaptive training. One issue that arises when estimating the noise model is a mismatch between the noise estimation approximation and final model compensation scheme. This paper proposes FA-style compensation where this mismatch is eliminated, though at the expense of a sensitivity to the initial noise estimates. EM-based discriminative adaptive training is evaluated on in-car and Aurora4 tasks. FA-style compensation is then evaluated in an incremental mode on the in-car task.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5947427
ICASSP
Keywords
Field
DocType
fa-style compensation,speech recognition,jud noise estimation,noise robustness,noise robust speech recognition,canonical models,adaptive training,em-based discriminative adaptive training,factor analysis,vts,aurora4 tasks,noise,speech,second order,canonical model,estimation
Pattern recognition,Noise measurement,Computer science,Load modeling,Speech recognition,Canonical model,Artificial intelligence,Discriminative model
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4577-0537-3
978-1-4577-0537-3
6
PageRank 
References 
Authors
0.63
6
2
Name
Order
Citations
PageRank
Federico Flego1556.19
Mark J. F. Gales23905367.45