This thesis investigates the design and implementation strategies for a Spanish speech recognition system. It utilizes the speech features of the 242 common Spanish mono-syllables as the major training and recognition methodology. A training database of twelve utterances per mono-syllable is established by applying Spanish pronunciation rules. These twelve utterances are collected through reading six rounds of the same mono-syllable with two different tones. The first pronounced pattern has high pitch of tone one, while the second one has falling pitch of tone four. Mel-frequency cepstral coefficients, linear predictive cepstral coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Sempron Processor 2800+ with 1.6GHz clock rate personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 86% can be reached for a 4217 Spanish phrase database. The average computation time for each phrase is about 1.5 seconds.

This thesis investigates the design and implementation strategies for a German speech recognition system. It utilizes the speech features of the 434 common German 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 cepstral coefficients, linear predictive cepstral coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Athlon X2-240 with 2.8 GHz clock rate personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 84% can be reached for a 3900 German phrase database. The average computation time for each phrase is within 1 second.

Britain established the status of maritime hegemony in 1588. The English language along with the British colonized activities was spread to North America, India, Africa and Australia. After the end of World War I in 1918, the U.S. became the most powerful nation in the world economy. And at the same time, the world financial center was shifted to New York from London. In 1945, the World War II ended, the U.S. further played indispensable role in each aspect of international politics, economy and technologies. The United Nation, founded on October 24, 1945, adopted English, Chinese, French, Spanish, Arabic as well as Russian as the six working languages. These historical events facilitated a succession of language expansion and caused English to be the most widely used international language. Beside the political, economic and technological superiority, Britain owns the largest comprehensive museum in the globe, the British Museum. This Museum was located in London, built in 1753, and more than 13 million cultural relics of archaeology from around the world were collected. Her cultural resources are remarkably rich. It is our objective to build a language system that can help us to learn English more effectively and to widen our vision of living at the same time. This thesis investigates the recognition rate improvement strategies for an English speech recognition system. It utilizes the speech features of the 989 common English mono-syllables as the major training and recognition methodology. A training database is established by reading each mono-syllable 14 rounds. Each one of the 989 mono-syllables is consecutively read with two different tones at alternate rounds. The odd pronounced rounds have high pitch of tone 1, while the even rounds have falling pitch of tone 4. The pitch period frame method is applied for enhancing the accuracy of end point detection. Mel-frequency cepstral coefficients, linear predictive cepstral coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. The number of HMM states is adjusted to 10 and the phonotactical rule is used for the recognition rate improvement. Under the Core ™ i5 CPU M450 notebook computer with 2.4GHz clock rate and Fedora 14 operating system environment, a 92.94% correct phrase recognition rate can be reached for a 6,812 English phrase database. The average computation time for each phrase is within 1.5 seconds.