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A Hybrid Design of Speech Recognition System for Chinese Names

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.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0906104-161603
Date06 September 2004
CreatorsHsu, Po-Min
ContributorsTsung Lee, Chih-Chien Chen, Chii-Maw Uang
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906104-161603
Rightsnot_available, Copyright information available at source archive

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