Dynamic estimation of a noise over estimation factor for Jacobian-based adaptation

In this paper we propose an enhancement of the Jacobian adaptation by estimating automatically a noise over estimation factor which yields to a closer approximation of Parallel model combination (PMC) than the traditional Jacobian adaptation. Noise over estimation factors are estimated at run-time f...

Full description

Saved in:
Bibliographic Details
Published in2002 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 1; pp. I-201 - I-204
Main Authors Cerisara, Christophe, Junqua, Jean-Claude, Rigazio, Luca
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2002
Subjects
Adaptation model
Hidden Markov models
Jacobian matrices
Testing
USA Councils
Online AccessGet full text

Cover

More Information
Summary:In this paper we propose an enhancement of the Jacobian adaptation by estimating automatically a noise over estimation factor which yields to a closer approximation of Parallel model combination (PMC) than the traditional Jacobian adaptation. Noise over estimation factors are estimated at run-time for a set of clustered Gaussians obtained on the training set. Experiments conducted on a French natural number database show that similar performance as PMC can be obtained at the expense of a slight increase in computational complexity as compared to Jacobian adaptation.
ISBN:9780780374027
0780374029
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2002.5743689