Recurrent neural networks for speech modeling and speech recognition

Describes a new method of utilizing recurrent neural networks (RNNs) for speech modeling and speech recognition. For each particular speech unit, a fully connected recurrent neural network is built such that the static and dynamic speech characteristics are represented simultaneously by a specific temporal pattern of neuron activation states. By using the temporal RNN output, an input utterance can be represented as a number of stationary speech segments, which may be related to the basic phonetic components of the speech unit. An efficient self-supervised training algorithm has been developed for the RNN speech model. The segmentation for input utterances and the statistical modeling for individual phonetic segments are performed interactively in this training process. Some experimental results are used to demonstrate how the proposed RNN speech model can be used effectively for automatic recognition of isolated speech utterances