Dynamic estimation of a noise over estimation factor for Jacobian-based adaptation
Christophe Cerisara, Jean-Claude Junqua, Luca Rigazio · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
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.