Linear hidden transformations for adaptation of hybrid ANN/HMM models

This paper focuses on the adaptation of Automatic Speech Recognition systems using Hybrid models combining Artificial Neural Networks (ANN) with Hidden Markov Models (HMM).Most adaptation techniques for ANNs reported in the literature consist in adding a linear transformation network connected to the input of the ANN. This paper describes the application of linear transformations not only to the input features, but also to the outputs of the internal layers. The motivation is that the outputs of an internal layer represent discriminative features of the input pattern suitable for the classification performed at the output of the ANN.In order to reduce the effect due to the lack of adaptation samples for some phonetic units we propose a new solution, called Conservative Training.Supervised adaptation experiments with different corpora and for different types of adaptation are described. The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations.