Two-Stage Speaker Adaptation of Hybrid Tied-Posterior Acoustic Models
Jan Stadermann, Gerhard Rigoll · 2006
In this paper, strategies are explored to adapt hybrid neural network/HMM systems based on the tied-posterior paradigm. We investigate the retraining of selected important parts of the neural network and a gradient based adaptation strategy for the HMM mixture coefficients based on maximizing the scaled likelihood. The paper presents the following innovations: first, it introduces one of the first adaptation methods for hybrid systems where the HMM component contributes significantly to the adaptation success; second, it presents a novel approach to the neural network's adaptation, based on the selection of suitable neurons for adaptation. Results on the WSJ speaker adaptation test show the capability of our methods to adapt to new speakers, especially in the case of adapting the neural net, and that both methods can be combined to achieve additional improvement of the word error rate in most cases.