This thesis investigates the design and implementation strategies for a Japanese speech recognition system. It utilizes the speech features of the 188 common Japanese mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Japanese pronunciation rules. These 10 utterances are collected through reading 5 rounds of 188 mono-syllables, where every mono-syllable is consecutively read twice in each round. Mel-frequency cepstrum coefficients, linear predicted 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 8.04 operating system environment, a correct phrase recognition rate of 87% can be reached for a 34,000 Japanese phrase database. The average computation time for each phrase is about 1.5 seconds.

This thesis investigates the design and implementation strategies for a English speech recognition system. Two speech inputting methods, the spelling inputting and the reading inputting, are implemented for English word recognition and query. Mel-frequency cepstrum coefficients, linear predicted cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 1.6 GHz personal computer and Ubuntu 8.04 operating system environment, a 95% correct recognition rate can be obtained for a 110 thousand English word database by the spelling inputting method; and a 93% correct recognition rate can be achieved for a 1,500 English word database by the reading inputting method. The average computation time for each word using either inputting method is about 1.5 seconds.