NDLI: A speech recognizer with low complexity based on RNN

Content Provider	IEEE Xplore Digital Library
Author	Kasper, K. Reininger, H. Wolf, D. Wust, H.
Copyright Year	1995
Description	Author affiliation: Inst. fur Angewandte Phys., Frankfurt Univ., Germany (Kasper, K.; Reininger, H.; Wolf, D.; Wust, H.)
Abstract	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.
Starting Page	272
Ending Page	281
File Size	539734
Page Count	10
File Format	PDF
ISBN	078032739X
DOI	10.1109/NNSP.1995.514901
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-08-31
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Recurrent neural networks Telephony Neurons Costs Remotely operated vehicles Mobile robots Vocabulary Robustness Bandwidth
Content Type	Text
Resource Type	Article

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