We evaluate the security of human voice password databases from an information theoretical point of view. More specifically, we provide a theoretical estimation on the amount of entropy in human voice when processed using the conventional GMM-UBM technologies and the MFCCs as the acoustic features. The theoretical estimation gives rise to a methodology for analyzing the security level in a corpus of human voice. That is, given a database containing speech signals, we provide a method for estimating the relative entropy (Kullback-Leibler divergence) of the database thereby establishing the security level of the speaker verification system. To demonstrate this, we analyze the YOHO database, a corpus of voice samples collected from 138 speakers and show that the amount of entropy extracted is less than 14-bits. We also present a practical attack that succeeds in impersonating the voice of any speaker within the corpus with a 98% success probability with as little as 9 trials. The attack will still succeed with a rate of 62.50% if 4 attempts are permitted. Further, based on the same attack rationale, we mount an attack on the ALIZE speaker verification system. We show through experimentation that the attacker can impersonate any user in the database of 69 people with about 25% success rate with only 5 trials. The success rate can achieve more than 50% by increasing the allowed authentication attempts to 20. Finally, when the practical attack is cast in terms of an entropy metric, we find that the theoretical entropy estimate almost perfectly predicts the success rate of the practical attack, giving further credence to the theoretical model and the associated entropy estimation technique.

Acoustic features of sound such as pitch, loudness, perceived duration and timbre have been shown to be related to emotion in regard to sound, demonstrating that an important connection between the perceived emotions and their timbres is lacking. This study investigates the relationship between acoustic features of sound and emotion in regard to timbre. In two experiments we investigated whether particular acoustic components of sound can predict timbre, and particular categories of emotion, and how these attributes are related. Two behavioral experiments related perceived emotion ratings with synthetically created sounds and International Affective Digitized Sounds (Bradley & Lang, 2007) sounds. Also, two timbre experiments found acoustic components of synthetically created sounds, and IADS. Regression analyses uncovered some relationships between emotion, timbre, and acoustic features of sound. Results indicate that emotion is perceived differently for synthetic instrumental sounds and IADS. Mel-frequency cepstral coefficients were a strong predictor of perceived emotion of instrumental sounds; however, this was not the case for the IADS. This difference lends itself to the idea that there is a strong relationship between emotion and timbre for instrumental sounds, perhaps in part because of their relationship to speech and the way these different sounds are processed.

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.

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.

IBM, a well-known computer giant, and Nuance, a renowned speech technology firm, have been offering numerous speech recognition applications in the recent years. The connections between these two companies and the automobile, communication, and other eight dominating industries, including banking, electronics, energy/utilities, medical/life science, insurance, media/entertainment, retail travel and transportation, are vastly expanded and flourished. Maturity of these speech technologies drives our lifestyle to a cozy level that we cannot imagine before. In April, 2011, the world class manufacturer Foxconn decided to invest 12 billion US dollars to build iPhone/iPad factories in Brazil, the largest Portuguese speaking country in the world. It is our objective to build a language system that can help us to learn Portuguese, to savor the beauty of their culture, and to widen our vision of travel and living. This thesis investigates the design and implementation strategies for a Portuguese speech recognition system. It utilizes the speech features of the 303 common Portuguese mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Portuguese 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 87.26% can be reached using phonotactical rules for a 3,900 vocabulary Portuguese phrase database. The average computation time for the Portuguese phrase system is less than 1.5 seconds, and the training time for the systems is about two hours.

Language plays an important role for understanding people, their history, culture and even technology. Many countries of the world have developed the technology of the outer space recently, and Russian is the top of the world. In 1998 Russia further launched Zarya, the first International Space Station (ISS) Module, to the outer space, and was deeply involved in the development of the ISS with the U.S.. Since the end of the World War Two, Russia has been one of the five Permanent Members in the United Nations. And then, she became one of the G8 members, an economical forum of eight industrially advanced nations. Because these informations, it is our objective to build a language system that can help us to learn Russian, to taste the beauty of her culture, and to widen our vision of technologies. This thesis investigates the design and implementation strategies for a Russian speech recognition system. It utilizes the speech features of the 514 common Russian mono-syllables as the major training and recognition methodology. The mono-syllable is established by applying Russian 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 2.2 GHz Athlon XP 2800+ personal computer and Ubuntu 9.04 operating system environment, correct phrase recognition rates of 86.90% and 94.83% can be reached using phonotactical rules for a 3,900 vocabulary Russian phrase database for TORFL (Test of Russian as a Foreign Language) and a 600 person name database for Russian. The average computation time for each system is less than 1.5 seconds, and the training time for the systems is about three hours.

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.