A speech recognizer with low complexity based on RNN

Speech recognition systems (SRS) designed for applications in low cost products, like telephones or in systems like autonomous vehicles, are faced with the demand for solutions with low complexity. A small vocabulary consisting of a few command words and the digits is sufficient for most of the applications but has to be recognized robustly. Here we report about investigations concerning the application of recurrent neural networks (RNN) for speaker independent recognition of speech signals with telephone bandwidth. An RNN-SRS with low complexity is developed which recognizes isolated words as well as connected digits in adverse conditions. We introduce locally recurrent neural networks (LRNN). LRNN are layered networks which have recurrent connections only between the neurons of a hidden layer and their n-nearest neighbours. The neurons of the input and the output layer have unidirectional and sparse connections to the hidden layer. In comparison to RNN the density of the connections is drastically reduced and long distance wiring could be avoided in VLSI realization.