This thesis investigates the design and implementation strategies for a French speech recognition system. It utilizes the speech features of the 425 common French mono-syllables as the major training and recognition methodology. A training database is established by reading each mono-syllable 12 times in 6 rounds. Every mono-syllable is consecutively read twice with different tones. The first pronounced pattern has high pitch of tone 1,while the second one has falling pitch of tone 4. Mel-frequency cepstrum coefficients, linear predictive cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Athlon xp 2800+ with clock rate 2.2GHz personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 86% can be reached for a 3850 French phrase database. The average computation time for each phrase is about 1.5 seconds.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0824110-152849 |
| Date | 24 August 2010 |
| Creators | Li, Chun-Ching |
| Contributors | Tsung Lee, Chih-Chien Chen, Xiao-Song Bo, Er-Hui Lu, 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-0824110-152849 |
| Rights | not_available, Copyright information available at source archive |
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