Adaptation of Hybrid ANN/HMM Using Weights Interpolation

Many techniques for speaker or channel adaptation have been successfully applied to automatic speech recognition. Most of these techniques have been proposed for the adaptation of hidden Markov models (HMMs). Far less proposals have been made for the adaptation of the artificial neural networks (ANNs) used in the hybrid HMM-ANN approach. This paper presents an adaptation technique for ANNs that, similar to the framework of MAP estimation, tries to exploit in the adaptation process prior information that is particularly useful to deal with the problem of sparse training data. We show that the integration of a priori information can be simply achieved by linear interpolation of the weights of an "a priori" network and of a speaker specific network. Good improvements with respect to the baseline results are reported evaluating this technique on the Wall Street Journal WSJ0 and WSJ1 databases and on TIMIT corpus using different amounts of adaptation data