MLP based hierarchical system for task adaptation in ASR

Abstract

We investigate a multilayer perceptron (MLP) based hierarchical approach for task adaptation in automatic speech recognition. The system consists of two MLP classifiers in tandem. A well-trained MLP available off-the-shelf is used at the first stage of the hierarchy. A second MLP is trained on the posterior features estimated by the first, but with a long temporal context of around 130 ms. By using an MLP trained on 232 hours of conversational telephone speech, the hierarchical adaptation approach yields a word error rate of 1.8% on the 600-word Phonebook isolated word recognition task. This compares favorably to the error rate of 4% obtained by the conventional single MLP based system trained with the same amount of Phonebook data that is used for adaptation. The proposed adaptation scheme also benefits from the ability of the second MLP to model the temporal information in the posterior features.