Abstract
A high performance neural-networks-based speech recognition system is presented in this paper. A new approach towards feature representation for speech recognition, named State Transition Matrix (STM), is proposed to address temporal varying problem in speech recognition. Using STM, we need only a single-layer perceptron neural network to perform speech recognition. Experimental results show that an overall accuracy of 95% and 87% was achieved for speaker-dependent isolated word recognition and multi-speaker-dependent isolated word recognition respectively.
| Original language | English |
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| Pages | 1527-1531 |
| Number of pages | 5 |
| Publication status | Published - 2001 |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 15 Jul 2001 → 19 Jul 2001 |
Conference
| Conference | International Joint Conference on Neural Networks (IJCNN'01) |
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| Country/Territory | United States |
| City | Washington, DC |
| Period | 15/07/2001 → 19/07/2001 |