Large Vocabulary Continuous Speech Recognition With Reservoir-Based Acoustic Models

Abstract

Thanks to research in neural network based acoustic modeling, progress in Large Vocabulary Continuous Speech Recognition (LVCSR) seems to have gained momentum recently. In search for further progress, the present letter investigates Reservoir Computing (RC) as an alternative new paradigm for acoustic modeling. RC unifies the appealing dynamical modeling capacity of a Recurrent Neural Network (RNN) with the simplicity and robustness of linear regression as a model for training the weights of that network. In previous work, an RC-HMM hybrid yielding very good phone recognition accuracy on TIMIT could be designed, but no proof was offered yet that this success would also transfer to LVCSR. This letter describes the development of an RC-HMM hybrid that provides good recognition on the Wall Street Journal benchmark. For the WSJ0 5k word task, word error rates of 6.2% (bigram language model) and 3.9% (trigram) are obtained on the Nov-92 evaluation set. Given that RC-based acoustic modeling is a fairly new approach, these results open up promising perspectives.