Arab world is one of the most spectacular regions in the earth, especially for her over 2,800 year history, Islamic religion and magnificent culture. She consists of 24 countries and territories where people speak Arabic. The population of Arabic speaking people is approximately 221 million, and ranked the fourth according to the 2009 statistics by Summer Institute of Linguistics, USA. Since 1973, petroleum embargoes, imposed by the Arab world, have influenced global economy and hurt national security seriously. This kind of fossil energy is still irreplaceable until efficient green energy alternative becomes feasible. It is our objective to build a language system that can help us to learn Arabic, to appreciate the beauty of her culture, and to widen our vision of religions. This thesis investigates the design and implementation strategies for an Arabic speech recognition system. It utilizes the speech features of the 302 common Arabic mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Arabic 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 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, correct phrase recognition rates of 86.31% and 93.90% can be reached respectively using phonotactical rules for a 3,600 vocabulary Arabic phrase database and a 590 person name database for Arabic figures. The average computation time for each system is less than 1 second, and the training time for the systems is about two hours.

The European Union (EU) established on November 1, 1993, according to the Maastricht Treaty signed on February 7, 1992. This economic and political community consists of 27 member states, primarily located in Europe. She operates through a supranational and intergovernmental system, including the European Commission, the Council, the Parliament and the Central Bank, to transfer herself from the joint economic development regions to the single market of economic and political integration. Italy is one of the six founding countries of the EU, also one of the G8 members, the eight industrially advanced nations in the world, and playing a force to be reckoned with. It is our objective to build a language system that can help us to learn Italian more effectively, to promote our competency of intercultural understanding, and to widen our vision of travel and living. This thesis investigates the design and implementation strategies for an Italian speech recognition system. It utilizes the speech features of the 370 common Italian mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Italian 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 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, correct phrase recognition rates of 88.35% and 89.32% can be reached using phonotactical rules for a 4,000 vocabulary Italian phrase database and a 3,304 word database for Italian Language Proficiency Test. The average computation time for each system is less than 1.5 seconds, and the training time for the systems is about two hours.

The Republic of Turkey, founded in 1923, is a well-known ancient country with abundant cultural heritage and great junction location of the Asian and European Continents. Istanbul is the largest city of this country with her old name Constantinople or Byzantium. She was established by Constantinus I Magnus in A.D. 330 during the era of the Roman Empire, to serve as a well-fortified castle like Rome. Numerous attractions on historical architecture, ancient music, gourmet cuisine, and art collections can be explored and appreciated. It is our objective to build a language system that can help us to learn Turkish, to savor the beauty of her culture, and to widen our vision of travel and living. This thesis investigates the design and implementation strategies for a Turkish speech recognition system. It utilizes the speech features of the 395 common Turkish mono-syllables as the major training and recognition methodology. A training database of 12 utterances per mono-syllable is established by applying Turkish pronunciation rules. These 12 utterances are collected through reading 6 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 predicted cepstral coefficients, and hidden Markov model are used as the two syllable feature models and the recognition model respectively. Under the AMD 2.8 GHz Athlon X2 2400 personal computer and Ubuntu 9.04 operating system environment, correct phrase recognition rates of 87.29% can be reached using phonotactical rules for a 3,644 vocabulary Turkish phrase database. The average computation time for the each system is less than 1.5 seconds, and the training time for the systems is about two hours.

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.

In recent years, with the microarray technique widely adopted, a large amount of biomedical literatures are published to provide a lot of useful information. However, some relationships among disease, genes and drug are still to be explored, since the authors only focus on part of the significant genes to the disease or the significant genes to the drug but not connect them to obtain new relationships. There are several methods proposed for finding out the hidden relationships, however many of them requires manual involvements. The main objective of this dissertation is to discover the hidden connections between human diseases and genes and the connections between drugs and the same genes. In order achieve this goal, the intermediate nodes (signification genes) must be found first. When a gene has more significant difference in observed group (abnormal patients) than in control group (normal persons), this gene is called significant genes to the disease. These signification genes often play a crucial role in cancer diagnosis and treatment. Via classifying the microarray gene expression data to find these significant genes, doctors can obtain the feasible and appropriate information for treatments that can give to the patients according to their cancer symptoms. A variety of existing classifiers have been proposed for this problem. However, most of them often work inefficiently when attributes grow up over thousands. To further improve the accuracy and the speed of the existing classifiers, a novel microarray attribute reduction scheme (MARS) is proposed for selecting significant genes to the disease. Experimental results demonstrate that combining the proposed scheme with multiclass support vector machine (MCSVM) obtains better performance than other different gene selection methods with the same MCSVM. In addition, the proposed scheme with MCSVM performs better than the results listed in the existing literature.. Furthermore, 19 of 22 genes selected by the proposed scheme in acute lymphoblastic leukemia and acute myeloid leukemia (AML-ALL) dataset are related to the AML and ALL diseases that have been reported in the literatures. Thus the proposed scheme not only can significantly reduce large amount of attributes (genes) for gene expression classification problem, but also increase the classification accuracy. MARS finds related gene set according to a threshold determined by using receiver operating characteristic (ROC) curve. However, it requires repeating the experiment many times to determine the best threshold. Hence, we propose a novel disease-oriented feature selection algorithm (DOFA) to improve MARS. DOFA uses the Genetic Algorithm (GA) in the selection method for automatic picking up the related genes and Support Vector Machine (SVM) and K-nearest-neighborhood (KNN) as the classifier. DOFA is tested on picking up related genes for AML-ALL and Colon datasets. For AML-ALL and Colon datasets, it selects 21 genes and 25 genes, respectively. Based on the literatures, it shows that 20 of 21 genes are related to the disease or cancers related for AML-ALL dataset and one of these genes is still uncertain. And 20 of 25 genes are directly related to the disease colon cancer or cancers related and 5 of these genes are still uncertain. Three more experiments are conducted to verify the discriminability of the genes selected by DOFA. Experimental results all indicate that DOFA obtains better performance than other competing methods. Thus DOFA not only can select the genes related to the diseases, but also increase the classification accuracy. After obtaining the significant gene group, we can further use these genes to obtain the hidden connections. We propose a high specificity literature mining method based on microarray expression profile for discovering hidden connections among disease, drug, and genes. The proposed method can automatically select related genes from the disease or drug microarray expression profiles, and use the disease names or the drug names and gene names or aliases of the selected genes to obtain the related abstract collections. An alias expansion scheme and a weight function are used to eliminate the unrelated literatures. We perform three scenarios to verify the proposed method. Experimental results show that using the proposed method can obtain the hidden connections among diseases, genes and drugs. The (ROC) curve shows that the proposed method can not only find the hidden connections between diseases and drugs but also have high specificity. Concluding this dissertation, our goal is to discover the hidden connections between the diseases and the drugs. In order to achieve this goal, we first proposed MARS to select the significant genes to the diseases. And then, we proposed DOFA to improve the ability of MARS. We proposed a high specificity literature mining method based on microarray expression profile for discovering the hidden connections among diseases, genes, and drugs. The proposed method combines the power of searching significant genes to the disease of DOFA to further obtain the hidden connections. Experimental results show that the proposed method not only can obtain the hidden connections among diseases, genes, and drugs, but also has high specificity.

According to the statistics of the Ministry of Education in 2006, there were about 1,000 students educated in more than 10 institutes of the arts administration field in Taiwan. Most of these institutes are in graduated level. However, in a survey of 517 full-time arts administrators of performing arts groups published by Council of Culture Affairs in 2007 showed there were only about 15% of them with master degrees. Besides personal interests of these administrators, this research want to clarify the situation: if these arts administrators have any other special personalities or abilities; therefore, if there are some differences between arts administrators¡¦ competencies of performing arts groups¡¦ requirements and the academic training of the arts administration and management. The researcher has approached this problem in three directions: the curriculum designs of the related master programs, the ideal competencies from literature review, and the interview with four personnel managers of performing arts cultivation teams. The analysis and comparison are done by this triangulation. The study finds that there are 32 competencies suggested by the past studies, and 30 important competencies are mentioned by the performing arts groups. On the other hand, the schools¡¦ courses can only cultivate 12 competencies, required by the performing arts groups. The result shows that the related master programs are more concentrated on visible competencies than hidden ones. They all can be improved to meet the working field required. Key words: performing arts group, arts administrator, curriculum design, hidden competency, visible competency

¡@¡@In this thesis, we use a hidden Markov model which can use a small amount of corpus to synthesize speech with certain quality to implement speech synthesis system for Chinese. More, the emotional speech are synthesized by the flexibility of the parametric speech in this model. We conduct model interpolation and model adaptation to synthesize speech from neutral to particular emotion without target speaker¡¦s emotional speech. In model adaptation, we use monophone-based Mahalanobis distance to select emotional models which are close to target speaker from pool of speakers, and estimate the interpolation weight to synthesize emotional speech. In model adaptation, we collect abundant of data training average voice models for each individual emotion. These models are adapted to specific emotional models of target speaker by CMLLR method. In addition, we design the Latin-square evaluation to reduce the systematic offset in the subjective tests, making results more credible and fair. We synthesize emotional speech include happiness, anger, sadness, and use Latin square design to evaluate performance in three part similarity, naturalness, and emotional expression respectively. According to result, we make a comprehensive comparison and conclusions of two method in emotional speech synthesis.

Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users¡¦ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information when they cannot describe the requirements precisely. The filtering techniques in recommender systems can be divided into content-based filtering (CBF) and collaborative filtering (CF). Although CF is shown to be superior over CBF in literature, personalized document recommendation relies more on CBF simply because of its text content in nature. Nevertheless, document recommendation task provides a good chance to integrate both techniques into a hybrid one, and enhance the overall recommendation performance. The objective of this research is thus to propose a hybrid filtering approach for personalized document recommendation. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to help us to either obtain robust document similarity in CF, or explore user profiles in CBF. Two experiments are conducted accordingly. The results show that our proposed approach outperforms other counterparts on the recommendation performance, which justifies the feasibility of our proposed approach in real applications.

Economy growth rate is an index of a nation¡¦s gross productivity. China, Russia and Thailand are a few nations whose economy growth rates exceed the global average. In the recent years, the rapid development in China, including the enhanced relation with Taiwan, has made her the member of the BRICS, the top five emerging countries in the world. Russia has been playing an important role in the international society during the past decades. She is not only the member of the G8, the group of eight major industrial nations, but also her language, Russian, is one of the six official languages in the United Nations. According to the statistics of the Taiwan Funds, Russia and Thailand are the top two countries in their investment growth. Thailand, located in the middle of the Southeast Peninsular, together with Malaysia and Philippines, are the three founding members of the ASEAN 10, the Association of Ten Southeast Asian Nations. Due to the industrial and household needs, Taiwan has offered job opportunities to foreign labors from the Southeast countries. Therefore, it is our objective to design a trilingual speech recognition system for Chinese, Russian and Thai to meet the needs of language learning and household living. This system utilizes 404 Chinese, 611 Russian and 123 Thai common mono-syllables, selected from their pronunciation rules, 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 88.87%, 84.31% and 87.58% can be reached using phonotactical rules for the 82,000 Chinese, 31,883 Russian and 3,809 Thai phrase database respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.66% correct language-phrase recognition rate can be obtained.

Mandarin Chinese, Taiwanese and Cantonese all belong to the Chinese language family. According to the statistics from Summer Institute of Linguistics, USA, Chinese language are spoken by over 1.2 billion population, ranked number one in the world. The regions where these three languages are spoken have been playing an important role for global economy. For example, Hong Kong and Taiwan all have flourishing harbors for international trade. Furthermore, Mandarin Chinese, Taiwanese and Cantonese are the most influential among the seven Chinese dialects. Mandarin Chinese was admitted as a language by the United Nations in the early years while Cantonese was accepted in 2006. Cantonese is spoken in many Western countries. She is the fourth language in Australia as well as the third language in Canada and America. From the phonetics point of view, these three languages are all tonal languages in which words or phrases uttered in different pitch or duration have distinct lexical meaning. This thesis investigates the design and implementation strategies for Chinese, Taiwanese and Cantonese. Based on their pronunciation rules and tonal properties, common mono-syllables for each language 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 Athlon XP 2800+ personal computer and Ubuntu 9.04 operating system environment, the correct recognition rates of 88.03%, 86.00% and 86.79% can be reached using phonotactical rules for the 82,000 Chinese, 5,129 Taiwanese and 3,051 Cantonese phrase database respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 97.66% correct language-phrase recognition rate can be obtained.