Modular neural networks exploit multiple front-ends to improve speech recognition systems

We have been investigating the possible advantages of a modular/ensemble neural network for acoustic modelling. We report experiments with ensembles of networks trained on data provided by different front-end preprocessing methods. As for previous work we train a network ensemble for each individual phone and combine the outputs of the ensemble using a further trained network. The combined system provides significant improvements for phone recognition and classification on the TIMIT corpus. Our results are now better than the best context-independent systems in the literature and close to the best context-dependent systems