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NOLISP
2005
Springer
14 years 1 months ago
Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition
Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the p...
Kevin M. Indrebo, Richard J. Povinelli, Michael T....
ICASSP
2009
IEEE
14 years 2 months ago
Modelling the prepausal lengthening effect for speech recognition: a dynamic Bayesian network approach
Speech has a property that the speech unit preceding a speech pause tends to lengthen. This work presents the use of a dynamic Bayesian network to model the prepausal lengthening ...
Ning Ma, Chris Bartels, Jeff A. Bilmes, Phil Green
ICASSP
2011
IEEE
12 years 11 months ago
Non-linear noise compensation for robust speech recognition using Gauss-Newton method
In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...
Yong Zhao, Biing-Hwang Juang
CSL
2010
Springer
13 years 7 months ago
Monaural speech separation and recognition challenge
Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific ca...
Martin Cooke, John R. Hershey, Steven J. Rennie
INTERSPEECH
2010
13 years 2 months ago
Artificial and online acquired noise dictionaries for noise robust ASR
Recent research has shown that speech can be sparsely represented using a dictionary of speech segments spanning multiple frames, exemplars, and that such a sparse representation ...
Jort F. Gemmeke, Tuomas Virtanen