Jacobian Adaptation with Continuous Noise Estimation for Real Speaker Verification Applications

Jacobian adaptation (JA) of the acoustic models is a fast adaptation technique that has been successfully used in both speech and speaker recognition. This technique adapts the acoustic models on the basis of the difference between the testing and the training noise conditions. For this reason, a noise reference of both the training and the testing phase is needed. In previous works, the noise conditions have been commonly supposed to be known or estimated from the first part of the signal. In this work we propose to obtain the noise references by using continuous noise estimation methods, which are more appropriate for real applications and can deal with non-stationary noises. Several noise estimation methods are compared: recursive averaging (RA), minimum statistics (MS) and minima controlled recursive averaging (MCRA). The obtained results show that these techniques are effective for JA