A Design of Trilingual Speech Recognition System for Chinese, Arabic and Dutch

• Main Authors: Ming-hui Tu, 涂銘暉
• Other Authors: Chih-Chien Chen
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/66821107148845449846

碩士 === 國立中山大學 === 電機工程學系研究所 === 100 === 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.