Hierarchical Bayes approach to adapting delta- and delta-delta cepstra

In most adaptation schemes, when limited amount of data is available, the estimation errors affect the feature coefficients which have a small dynamic range i.e. delta and delta-delta coefficients. On the other hand, adapting only the cepstral coefficients compromises the performance when large amounts of data are available. In this paper we present a novel approach to solving this problem based on hierarchical Bayesian adaptation. First the delta parameters are cast as transformations of the cepstra. Then the cepstra are adapted using Bayesian techniques. The parameters of posterior distribution of the cepstra are transformed to get the hyperparameters of the of the delta coefficients which are then adapted. In this paper, the solution is presented in the framework of Bayesian predictive adaptation, but the approach is general, hence it can be applied in any other adaptation framework. Preliminary results are presented that demonstrate the effectiveness of the new method.