This paper describes an isolated-word and speaker-independent Mandarin digit speech recognition system based on Backpropagation Neural Networks(BPNN). The recognition rate will achieve up to 95%. When the system was applied to a new user with adaptive modification method, the recognition rate will be higher than 99%. In order to implement the speech recognition system on Digital Signal Processors (DSP) we use a neuron-cancellation rule in accordance with BPNN. The system will cancel about 1/3 neurons and reduce 20%¡ã40% memory size under the rule. However, the recognition rate can still achiever up to 85%. For the output structure of the BPNN, we present a binary-code to supersede the one-to-one model. In addition, we use a new ideal about endpoint detection algorithm for the recoding signals. It can avoid disturbance without complex computations.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0706102-135328 |
| Date | 06 July 2002 |
| Creators | Chen, Sung-Lin |
| Contributors | I-Chih Kao, Chin-Ching Huang, Yin-Chin Wu, Yung-Chun Wu, Tzuen-Lih Chern |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706102-135328 |
| Rights | unrestricted, Copyright information available at source archive |
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