Gao Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 79-82). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Bibliographic Notes --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- What is Transliteration? --- p.1 / Chapter 1.2 --- Existing Problems --- p.2 / Chapter 1.3 --- Objectives --- p.4 / Chapter 1.4 --- Outline --- p.4 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Source-channel Model --- p.6 / Chapter 2.2 --- Transliteration for English-Chinese --- p.8 / Chapter 2.2.1 --- Rule-based Approach --- p.8 / Chapter 2.2.2 --- Similarity-based Framework --- p.8 / Chapter 2.2.3 --- Direct Semi-Statistical Approach --- p.9 / Chapter 2.2.4 --- Source-channel-based Approach --- p.11 / Chapter 2.3 --- Chapter Summary --- p.14 / Chapter 3 --- Transliteration Baseline --- p.15 / Chapter 3.1 --- Transliteration Using IBM SMT --- p.15 / Chapter 3.1.1 --- Introduction --- p.15 / Chapter 3.1.2 --- GIZA++ for Transliteration Modeling --- p.16 / Chapter 3.1.3 --- CMU-Cambridge Toolkits for Language Modeling --- p.21 / Chapter 3.1.4 --- Re Write Decoder for Decoding --- p.21 / Chapter 3.2 --- Limitations of IBM SMT --- p.22 / Chapter 3.3 --- Experiments Using IBM SMT --- p.25 / Chapter 3.3.1 --- Data Preparation --- p.25 / Chapter 3.3.2 --- Performance Measurement --- p.27 / Chapter 3.3.3 --- Experimental Results --- p.27 / Chapter 3.4 --- Chapter Summary --- p.28 / Chapter 4 --- Direct Transliteration Modeling --- p.29 / Chapter 4.1 --- Soundness of the Direct Model一Direct-1 --- p.30 / Chapter 4.2 --- Alignment of Phoneme Chunks --- p.31 / Chapter 4.3 --- Transliteration Model Training --- p.33 / Chapter 4.3.1 --- EM Training for Symbol-mappings --- p.33 / Chapter 4.3.2 --- WFST for Phonetic Transition --- p.36 / Chapter 4.3.3 --- Issues for Incorrect Syllables --- p.36 / Chapter 4.4 --- Language Model Training --- p.36 / Chapter 4.5 --- Search Algorithm --- p.39 / Chapter 4.6 --- Experimental Results --- p.41 / Chapter 4.6.1 --- Experiment I: C.A. Distribution --- p.41 / Chapter 4.6.2 --- Experiment II: Top-n Accuracy --- p.41 / Chapter 4.6.3 --- Experiment III: Comparisons with the Baseline --- p.43 / Chapter 4.6.4 --- Experiment IV： Influence of m Candidates --- p.43 / Chapter 4.7 --- Discussions --- p.43 / Chapter 4.8 --- Chapter Summary --- p.46 / Chapter 5 --- Improving Direct Transliteration --- p.47 / Chapter 5.1 --- Improved Direct Model´ؤDirect-2 --- p.47 / Chapter 5.1.1 --- Enlightenment from Source-Channel --- p.47 / Chapter 5.1.2 --- Using Contextual Features --- p.48 / Chapter 5.1.3 --- Estimation Based on MaxEnt --- p.49 / Chapter 5.1.4 --- Features for Transliteration --- p.51 / Chapter 5.2 --- Direct-2 Model Training --- p.53 / Chapter 5.2.1 --- Procedure and Results --- p.53 / Chapter 5.2.2 --- Discussions --- p.53 / Chapter 5.3 --- Refining the Model Direct-2 --- p.55 / Chapter 5.3.1 --- Refinement Solutions --- p.55 / Chapter 5.3.2 --- Direct-2R Model Training --- p.56 / Chapter 5.4 --- Evaluation --- p.57 / Chapter 5.4.1 --- Search Algorithm --- p.57 / Chapter 5.4.2 --- Direct Transliteration Models vs. Baseline --- p.59 / Chapter 5.4.3 --- Direct-2 vs. Direct-2R --- p.63 / Chapter 5.4.4 --- Experiments on Direct-2R --- p.65 / Chapter 5.5 --- Chapter Summary --- p.71 / Chapter 6 --- Conclusions --- p.72 / Chapter 6.1 --- Thesis Summary --- p.72 / Chapter 6.2 --- Cross Language Applications --- p.73 / Chapter 6.3 --- Future Work and Directions --- p.74 / Chapter A --- IPA-ARPABET Symbol Mapping Table --- p.77 / Bibliography --- p.82

No description available.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 110-123). Also available in electronic version.

Revision of the author's thesis--Kyoto University. / Includes bibliographical references (p. [177]-190 and index.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 174-191).

Originally presented as the author's thesis, Bonn, 1969. / Bibliography: p. 117-125.

Thesis (Ph. D.)--University of California, San Diego, 1997. / Vita. Includes bibliographical references (leaves 298-309).

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Mark Costello; Committee Member: Eric Johnson; Committee Member: Frank Fresconi; Committee Member: Olivier Bauchau; Committee Member: Peter Plostins. Part of the SMARTech Electronic Thesis and Dissertation Collection.

This thesis investigates translation tools and technologies in the Welsh language context and provides translators working in the Welsh-English language pair with a method of evaluation of Translation Memory (TM).

Thesis (M. Sc. (Computer Science)) -- University of Limpopo, 2019 / The pronunciation of words and phrases in any language involves careful manipulation of linguistic features. Factors such as age, motivation, accent, phonetics, stress and intonation sometimes cause a problem of inappropriate or incorrect pronunciation of words from non-native languages. Pronunciation of words using different phonological rules has a tendency of changing the meaning of those words. This study presents the development of an automatic pronunciation assistant system for under-resourced languages of Limpopo Province, namely, Sepedi, Xitsonga, Tshivenda and isiNdebele. The aim of the proposed system is to help non-native speakers to learn appropriate and correct pronunciation of words/phrases in these under-resourced languages. The system is composed of a language identification module on the front-end side and a speech synthesis module on the back-end side. A support vector machine was compared to the baseline multinomial naive Bayes to build the language identification module. The language identification phase performs supervised multiclass text classification to predict a person’s first language based on input text before the speech synthesis phase continues with pronunciation issues using the identified language. The speech synthesis on the back-end phase is composed of four baseline text-to-speech synthesis systems in selected target languages. These text-to-speech synthesis systems were based on the hidden Markov model method of development. Subjective listening tests were conducted to evaluate the performance of the quality of the synthesised speech using a mean opinion score test. The mean opinion score test obtained good performance results on all targeted languages for naturalness, pronunciation, pleasantness, understandability, intelligibility, overall quality of the system and user acceptance. The developed system has been implemented on a “real-live” production web-server for performance evaluation and stability testing using live data.