China, Italy and Iran are seemingly quite different in language, history, culture and economy. However, there have been existed mutual interactions among these three countries during the past age. In the fourth century, the Chinese Northern Wei Dynasty established close relation with the Persian Empire, located in Iran today. Persian language is also called Farsi in her native name. The unearthed silver bowls from China in the recent years showed similar appearance and material with the Sassanid-Persian silverware of Iran. Archaeologists found that ancient China and Iran used to be close international trading partners. In the thirteenth century, Marco-Polo, an Italian travel adventurer and merchant, visited Chinese Yuan Dynasty, and wrote a marvelous book ¡§The Travels of Marco-Polo¡¨. Fantastic experiences in China were depicted in this journal, and these initiated the Sino-Italian relation in the early days. Armani suits and Ferrari super racers become the oriental passion to the Italy in the Modern China, and this may represent the achievement of Asian-European culture exchange. Therefore, it is our objective to design a trilingual speech recognition system to help us to learn Chinese, Italian and Farsi languages. Linear predicted cepstral coefficients, Mel-frequency cepstral coefficients, hidden Markov model and phonotactics are used in this system as the two syllable feature models and the recognition model respectively. For the Chinese system, a 2,699 two-syllable words database is used as the training corpus. For the Italian and Farsi systems, a database of 10 utterances per mono-syllable is established by applying their pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with tone 1 and tone 4. The correct recognition rates of 87.54%, 87.48%, and 90.33% can be reached for the 82,000 Chinese, 27,900 Italian, and 4,000 Farsi phrase databases respectively. The computation time for each system is within 1.5 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67 % correct language-phrase recognition rate can be obtained with the computation time about 2 seconds.

The BRICS, Brazil, Russia, India, China and South Africa, have been making a significant amount of contribution to the global economy growth in the past few years. China possesses not only the largest population, but also the most splendid history in the world. During the recent years, the rapid development on all respects, including the enhanced economic trade with Taiwan, has made China in the line of the Super Powers. Brazil is the largest Portuguese speaking country in the world, where the world class manufacturer Foxconn Technology decided to build Apple iPad/iPhone factory in 2011. India has been flourishing in software, tele-communications and aviation industries since last decade. Offshore outsourcing consulting is so popular due to cost-down policy of the Western companies. Chinese, Portuguese and Hindi speaking population are over 1.573 billion, and account for over 22% of the world population. Therefore, it is our objective to establish a trilingual speech recognition system to help verbal communication and cultural understanding among languages. This thesis investigates the design and implementation strategies for a trilingual speech recognition system for Chinese, Portuguese and Hindi. Based on their pronunciation rules, the 404 Chinese, 515 Portuguese and 244 Hindi common mono-syllables are selected and utilized as the major speech training and recognition methodology. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, and hidden Markov model are used as the two syllable feature models and the recognition model respectively. Under the AMD 2.2 GHz Athlon XP 2800+ personal computer and Ubuntu 9.04 operating system environment, the correct phrase recognition rates of 87.69%, 85.14% and 86.74% can be reached using phonotactical rules for the 82,000 Chinese, 30,000 Portuguese and 3,900 Hindi phrase database respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be reached. The average computation time for each system is within 2 seconds.

Chinese as well as Arabic is one of the six official languages in the United Nations. The population of Chinese is over 1.2 billion, ranked number one in the world. Arabic, a language used in the Arab World, has a more than 2,800 year history. Her religion, culture and oil economy have been making far-reaching effects around the globe. The worldwide energy supply greatly relies on the petroleum from the Arab World. Netherland, whose official language is Dutch, has been an international trading power since ancient time. She has become an industrial giant today. Recently, European-study-abroad is getting more popular, many famous Netherland universities offer opportunities for foreign students. Therefore, it is our objective to design a trilingual speech recognition system to help us learn Chinese, Arabic and Dutch, as well as appreciate their profound history and beautiful culture. This thesis investigates the design and implementation strategies for a Chinese, Arabic and Dutch speech recognition system. A 2,699 two-syllable recorded words database is utilized as the Chinese training corpus. For the Arabic and Dutch systems, 396 and 205 common mono-syllables are selected respectively as the major training and recognition methodology. Each mono-syllable is uttered twice with tone 1 and tone 4, and ten training patterns are used for system implementation. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, hidden Markov model and phonotactics are applied as the two syllable feature models and the recognition model respectively. The correct recognition rates of 90.17%, 84.65%, and 86.69% can be reached for the 82,000 Chinese, 31,000 Arabic, and 3,600 Dutch phrase databases respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67 % correct language-phrase recognition rate can be obtained. The computation time for each system is about 2 seconds.

According to the statistics of Summer Institute of Linguistics, USA, there are about 7,000 languages in the world. Chinese, Hakka and Swedish are all the first 100 most popular languages. Chinese is spoken in Taiwan, Mainland China, Hong Kong and Macau. Hakka is the second popular dialect in Taiwan. The population is only less than that of Taiwanese. The ancestors of Hakka are from the Han people in Honan, China. Hakka culture has been cultivated by enormous migrations since the fourth century, and transformed to represent the tradition. Taiwan and Sweden are developed, free and democratic countries, with similar level of living standard. The ancestors of Sweden are from the Germanic peoples in Northern Europe. Swedish has been also evolved and transformed by massive migrations since the ninth century, sharing the analogous evolution route with Chinese and Hakka. Therefore, it is our objective to establish a trilingual speech recognition system to help verbal communication among languages in the global economic arena. This thesis investigates the design and implementation strategies for a trilingual speech recognition system for Chinese, Hakka and Swedish. Based on their pronunciation rules, the 404 Chinese, 204 Hakka and 369 Swedish common mono-syllables are selected as the major speech training and recognition methodology. A 2,699 two-syllable words database is recorded as the Chinese training corpus. The five rounds with four tones and six rounds with two tones training strategies are used for Hakka and Swedish respectively. Correct rates of 92.29%, 90.70% and 89.09% can be reached for the 82,000 Chinese, 3,900 Hakka and 3,750 Swedish phrase database respectively. Besides, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67% correct language-phrase recognition rate can be obtained. The average computation time for each system is within 2 seconds.

In this thesis, both Turkish and Tamil, a language spoken in southern India and Sri Lanka, are studied in addition to Mandarin Chinese. It is hoped that the history, culture, and economy behind each language can be acquainted, tasted and appreciated during the learning process. In the ancient Chinese Han and Tang Dynasties, the ¡§Silk Road¡¨ played the most magnificent role to connect among the Oriental China, the Western Turkey and the Southern India as the international trading corridor. In this modern era, Turkey and India are both the most important cotton exporting countries. Moreover, China, Turkey and India have been showing their potential to the newly emerging markets in the world. Therefore, a trilingual speech recognition system is developed and implemented to help us to learn Chinese, Turkish and Tamil, as well as to enhance our understanding to their history and culture. In this trilingual system, linear predicted cepstral coefficients, Mel-frequency cepstral coefficients, hidden Markov model and phonotactics are used as the two syllable feature models and the recognition model respectively. For the Chinese system, a 2,699 two-syllable words database is used as the training corpus. For the Turkish and Tamil systems, a database of 10 utterances per mono-syllable is established by applying their pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with tone 1 and tone 4. The correct rates of 88.30%, 84.21%, and 88.74% can be reached for the 82,000 Chinese, 30,795 Turkish, and 3,500 Tamil phrase databases respectively. The computation time for each system is within 1.5 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be reached with the computation time less than 2 seconds.

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.

A Mandarin speech recognition system for addresses based on MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM is a doubly stochastic process describing the ways of pronunciation by recording the state transitions according to the time-varing properties of the speech signal. In order to simplify the system design and reduce the computational cost, the mono-syllable structure information in Mandarin is used by incorporating both mono-syllable recognizor and HMM for our system. For the speaker-dependent case, Mandarin address inputting can be accomplished within 60 seconds and 98% correct identification rate can be achieved in the laboratory environment.

A Mandarin speech recognition system for addresses in Taiwan, based on end-point detection, MFCC and HMM, is proposed and implemented in this thesis. It includes both phrase and monosyllable recognition tasks. For the phrase recognition part, we select the initial candidates before the final recognition stage to tremendously reduce the computational time. On the other side, for the monosyllable recognition part, we further refine the recognition details to improve the correct rate under easily confused circumstances. The final system can achieve 85% correct identification rate, and the address recognition can be completed within 2 seconds in the laboratory environment for speaker-dependent case.

A system of Chinese resume by speech construction is developed by the use of a novel segmentation mechanism and the classical Hidden Markov Model. The recognition system is based on both mono-syllable HMM's and speech-text alignment schemes. Experimental results indicate that the amount of training materials used for feature extraction can be greatly reduced, and the text content of the recorded speech training data can be different from those of the recognition tasks as well. Each phrase in the resume can be identified within one second, that is approximately the same as the graduate did last year. Furthermore, the user interface of the resume system has been redesigned and polished by the GTK toolkit in order to enable event-driven X-window operations.

A design of speech recognition system for Chinese names of historical figures around the world is proposed in this thesis. A speech database of approximately forty-six thousand Chinese names is collected and recorded twice for system evaluation. This system applies Mel-frequency cepstrum coefficients, monosyllable HMM¡¦s and speech-text alignment scheme to accomplish initial candidate selection. A Mandarin pitch identification mechanism is then followed to increase the correct rate and obtain the final answer. The experimental results indicate that a 90% correct identification rate can be achieved, under the condition that the first session recording material is used for training and the second one for testing. For the speaker dependent case, the correct name can be recognized within 1.5 seconds, using a PC with an Intel Celeron 2.4 GHz CPU and RedHat Linux 9.0 Operation System.