A speech recognition system for Chinese names based on Karhunen Loeve transform (KLT), MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. KLT is the optimal transform in minimum mean square error and maximal energy packing sense to reduce data. HMM is a stochastic approach which characterizes many of the variability in speech signal by recording the state transitions. For the speaker-dependent case, the correct identification rate can be achieved 93.97% within 3 seconds in the laboratory environment.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0906104-161603 |
| Date | 06 September 2004 |
| Creators | Hsu, Po-Min |
| Contributors | Tsung Lee, Chih-Chien Chen, Chii-Maw Uang |
| 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-0906104-161603 |
| Rights | not_available, Copyright information available at source archive |
Page generated in 0.0021 seconds