| Summary: | 碩士 === 國立中山大學 === 電機工程學系研究所 === 98 === This thesis investigates the design and implementation strategies for a Korean speech recognition system. It utilizes the speech features of the common Korean mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Korean pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables 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 cepstral coefficients, linear predictive cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 2.4 GHz personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 92.25% can be reached for a 4865 Korean phrase database. The average computation time for each phrase is about 1.5 seconds.
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