Global ETD Search

101	A System Design of Chinese Resume by Speech Construction Chen, Yue-sheng 28 August 2006 (has links) 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. Speech-text alignment Hidden Markov model(HMM)
102	A Design of Speech Recognition System for Chinese Names of Historical Figures Around the World Lin, Wei-Ci 07 September 2006 (has links) 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. Hidden Markov model(HMM) Endpoint detection
103	A Design of Multi-Session, Text Independent, TV-Recorded Audio-Video Database for Speaker Recognition Wang, Long-Cheng 07 September 2006 (has links) A four-session text independent, TV-recorded audio-video database for speaker recognition is collected in this thesis. The speaker data is used to verify the applicability of a design methodology based on Mel-frequency cepstrum coefficients and Gaussian mixture model. Both single-session and multi-session problems are discussed in the thesis. Experimental results indicate that 90% correct rate can be achieved for a single-session 3000-speaker corpus while only 67% correct rate can be obtained for a two-session 800-speaker dataset. The performance of a multi-session speaker recognition system is greatly reduced due to the variability incurred in the recording environment, speakers¡¦ recording mood and other unknown factors. How to increase the system performance under multi-session conditions becomes a challenging task in the future. And the establishment of such a multi-session large-scale speaker database does indeed play an indispensable role in this task. Speaker recognition Text independent Vector quantization Gaussian mixture model Mel-frequency cepstrum coefficients
104	A Feature Design System for Speaker Independent Phrase Recognition Huang, Ming-Chong 15 June 2001 (has links) A novel phrase recognition method is proposed. It eliminates the speech difference between intraspeaker or interspeaker by transform phrases to difference subspace. A new endpoint detection method is also proposed, it can detection the human speech signal more effectively. All methods are test and verify at Microsoft Windows environment. Signal Space Difference Subspace Cepstrum Energy-Entropy Feature Phrase Recognition Mel-Cepstrum
105	A design of text-independent medium-size speaker recognition system Zheng, Shun-De 13 September 2002 (has links) This paper presents text-independent speaker identification results for medium-size speaker population sizes up to 400 speakers for TV speech and TIMIT database . A system based on Gaussian mixture speaker models is used for speaker identification, and experiments are conducted on the TV database and TIMIT database. The TV-Database results show medium-size population performance under TV conditions. These are believed to be the first speaker identification experiments on the complete 400 speaker TV databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 94.5% on the TV databases, respectively and 98.5% on the TIMIT database . Gaussian mixture model Vector quantization Speaker recognition Mel-frequency cepstrum coefficients
106	A Design and Applications of Mandarin Keyword Spotting System Hou, Cheng-Kuan 11 August 2003 (has links) A Mandarin keyword spotting system based on MFCC, discrete-time HMM and Viterbi algorithm with DTW is proposed in this thesis. Joining with a dialogue system, this keyword spotting platform is further refined to a prototype of natural speech patient registration system of Kaohsiung Veterans General Hospital. After the ID number is asked by the computer-dialogue attendant in the registration process, the user can finish all relevant works in one sentence. Functions of searching clinical doctors, making and canceling registration are all built in this system. In a laboratory environment, the correct rate of this speaker-independent patient registration system can reach 97% and all registration process can be completed within 75 seconds. Mel-frequency cepstrum coefficients phrase recognition Dynamic Time Warping Keyword spotting Hidden Markov model
107	A Design of Japanese Speech Recognition System Chen, Meng-yang 24 August 2009 (has links) This thesis investigates the design and implementation strategies for a Japanese speech recognition system. It utilizes the speech features of the 188 common Japanese mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Japanese pronunciation rules. These 10 utterances are collected through reading 5 rounds of 188 mono-syllables, where every mono-syllable is consecutively read twice in each round. Mel-frequency cepstrum coefficients, linear predicted 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 8.04 operating system environment, a correct phrase recognition rate of 87% can be reached for a 34,000 Japanese phrase database. The average computation time for each phrase is about 1.5 seconds. Linear predicted cepstrum coefficients Hidden Markov model Speech recognition Mel-frequency cepstrum coefficients
108	A Design of Taiwanese Speech Recognition System Jhu, Hao-fu 24 August 2009 (has links) This thesis investigates the design and implementation strategies for a Taiwanese speech recognition system. It adopts a 4 plus 1¡]five times¡^recording strategy, where the 1st four recordings are used for speech feature training and the last recording for speech recognition simulation. Mel-frequency cepstrum coefficients and hidden Markov model are used as the feature model and the recognition model respectively. Under the Intel Celeron 2.4 GHz personal computer and Red Hat Linux 9.0 operating system environment, a correct phrase recognition rate of 90% can be reached for a 4200 Taiwanese phrase database. Speech recognition Mel-frequency cepstrum coefficients Gaussian distribution Hidden Markov model
109	A Design of English Speech Recognition System Chen, Yung-ming 24 August 2009 (has links) This thesis investigates the design and implementation strategies for a English speech recognition system. Two speech inputting methods, the spelling inputting and the reading inputting, are implemented for English word recognition and query. Mel-frequency cepstrum coefficients, linear predicted cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 1.6 GHz personal computer and Ubuntu 8.04 operating system environment, a 95% correct recognition rate can be obtained for a 110 thousand English word database by the spelling inputting method; and a 93% correct recognition rate can be achieved for a 1,500 English word database by the reading inputting method. The average computation time for each word using either inputting method is about 1.5 seconds. Linear predicted cepstrum coefficients Hidden Markov model Mel frequency cepstrum coefficients Speech recognition
110	Aktivität endogener Retroviren in Tumorgeweben von Primaten / Activity of endogenous retroviruses in tumour tissues of primates Keiner, Nadine 29 June 2009 (has links) No description available. 570 Biowissenschaften, Biologie Mathematics and Computer Science HERV-K HERV-K-MEL HML-6 HML-2 HML-3 Melanom SIV RNAi Bcl-2 Caspase 8 SK-Mel-28 A-375 NHEM-M2 HERV-K HERV-K-MEL HML-6 HML-2 HML-3 melanoma SIV RNAi Bcl-2 Caspase 8 SK-Mel-28 A-375 NHEM-M2 WU WJ WF

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