Chan Yeuk Chi Joyce. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Previous Work on Code-switching Speech Recognition --- p.2 / Chapter 1.2.1 --- Keyword Spotting Approach --- p.3 / Chapter 1.2.2 --- Translation Approach --- p.4 / Chapter 1.2.3 --- Language Boundary Detection --- p.6 / Chapter 1.3 --- Motivations of Our Work --- p.7 / Chapter 1.4 --- Methodology --- p.8 / Chapter 1.5 --- Thesis Outline --- p.10 / Chapter 1.6 --- References --- p.11 / Chapter Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition for Cantonese and English --- p.14 / Chapter 2.1 --- Basic Theory of Speech Recognition --- p.14 / Chapter 2.1.1 --- Feature Extraction --- p.14 / Chapter 2.1.2 --- Maximum a Posteriori (MAP) Probability --- p.15 / Chapter 2.1.3 --- Hidden Markov Model (HMM) --- p.16 / Chapter 2.1.4 --- Statistical Language Modeling --- p.17 / Chapter 2.1.5 --- Search A lgorithm --- p.18 / Chapter 2.2 --- Word Posterior Probability (WPP) --- p.19 / Chapter 2.3 --- Generalized Word Posterior Probability (GWPP) --- p.23 / Chapter 2.4 --- Characteristics of Cantonese --- p.24 / Chapter 2.4.1 --- Cantonese Phonology --- p.24 / Chapter 2.4.2 --- Variation and Change in Pronunciation --- p.27 / Chapter 2.4.3 --- Syllables and Characters in Cantonese --- p.28 / Chapter 2.4.4 --- Spoken Cantonese vs. Written Chinese --- p.28 / Chapter 2.5 --- Characteristics of English --- p.30 / Chapter 2.5.1 --- English Phonology --- p.30 / Chapter 2.5.2 --- English with Cantonese Accents --- p.31 / Chapter 2.6 --- References --- p.32 / Chapter Chapter 3 --- Code-mixing and Code-switching Speech Recognition --- p.35 / Chapter 3.1 --- Introduction --- p.35 / Chapter 3.2 --- Definition --- p.35 / Chapter 3.2.1 --- Monolingual Speech Recognition --- p.35 / Chapter 3.2.2 --- Multilingual Speech Recognition --- p.35 / Chapter 3.2.3 --- Code-mixing and Code-switching --- p.36 / Chapter 3.3 --- Conversation in Hong Kong --- p.38 / Chapter 3.3.1 --- Language Choice of Hong Kong People --- p.38 / Chapter 3.3.2 --- Reasons for Code-mixing in Hong Kong --- p.40 / Chapter 3.3.3 --- How Does Code-mixing Occur? --- p.41 / Chapter 3.4 --- Difficulties for Code-mixing - Specific to Cantonese-English --- p.44 / Chapter 3.4.1 --- Phonetic Differences --- p.45 / Chapter 3.4.2 --- Phonology difference --- p.48 / Chapter 3.4.3 --- Accent and Borrowing --- p.49 / Chapter 3.4.4 --- Lexicon and Grammar --- p.49 / Chapter 3.4.5 --- Lack of Appropriate Speech Corpus --- p.50 / Chapter 3.5 --- References --- p.50 / Chapter Chapter 4 --- Data Collection --- p.53 / Chapter 4.1 --- Data Collection --- p.53 / Chapter 4.1.1 --- Corpus Design --- p.53 / Chapter 4.1.2 --- Recording Setup --- p.59 / Chapter 4.1.3 --- Post-processing of Speech Data --- p.60 / Chapter 4.2 --- A Baseline Database --- p.61 / Chapter 4.2.1 --- Monolingual Spoken Cantonese Speech Data (CUMIX) --- p.61 / Chapter 4.3 --- References --- p.61 / Chapter Chapter 5 --- System Design and Experimental Setup --- p.63 / Chapter 5.1 --- Overview of the Code-mixing Speech Recognizer --- p.63 / Chapter 5.1.1 --- Bilingual Syllable / Word-based Speech Recognizer --- p.63 / Chapter 5.1.2 --- Language Boundary Detection --- p.64 / Chapter 5.1.3 --- Generalized Word Posterior Probability (GWPP) --- p.65 / Chapter 5.2 --- Acoustic Modeling --- p.66 / Chapter 5.2.1 --- Speech Corpus for Training of Acoustic Models --- p.67 / Chapter 5.2.2 --- Features Extraction --- p.69 / Chapter 5.2.3 --- Variability in the Speech Signal --- p.69 / Chapter 5.2.4 --- Language Dependency of the Acoustic Models --- p.71 / Chapter 5.2.5 --- Pronunciation Dictionary --- p.80 / Chapter 5.2.6 --- The Training Process of Acoustic Models --- p.83 / Chapter 5.2.7 --- Decoding and Evaluation --- p.88 / Chapter 5.3 --- Language Modeling --- p.90 / Chapter 5.3.1 --- N-gram Language Model --- p.91 / Chapter 5.3.2 --- Difficulties in Data Collection --- p.91 / Chapter 5.3.3 --- Text Data for Training Language Model --- p.92 / Chapter 5.3.4 --- Training Tools --- p.95 / Chapter 5.3.5 --- Training Procedure --- p.95 / Chapter 5.3.6 --- Evaluation of the Language Models --- p.98 / Chapter 5.4 --- Language Boundary Detection --- p.99 / Chapter 5.4.1 --- Phone-based LBD --- p.100 / Chapter 5.4.2 --- Syllable-based LBD --- p.104 / Chapter 5.4.3 --- LBD Based on Syllable Lattice --- p.106 / Chapter 5.5 --- "Integration of the Acoustic Model Scores, Language Model Scores and Language Boundary Information" --- p.107 / Chapter 5.5.1 --- Integration of Acoustic Model Scores and Language Boundary Information. --- p.107 / Chapter 5.5.2 --- Integration of Modified Acoustic Model Scores and Language Model Scores --- p.109 / Chapter 5.5.3 --- Evaluation Criterion --- p.111 / Chapter 5.6 --- References --- p.112 / Chapter Chapter 6 --- Results and Analysis --- p.118 / Chapter 6.1 --- Speech Data for Development and Evaluation --- p.118 / Chapter 6.1.1 --- Development Data --- p.118 / Chapter 6.1.2 --- Testing Data --- p.118 / Chapter 6.2 --- Performance of Different Acoustic Units --- p.119 / Chapter 6.2.1 --- Analysis of Results --- p.120 / Chapter 6.3 --- Language Boundary Detection --- p.122 / Chapter 6.3.1 --- Phone-based Language Boundary Detection --- p.123 / Chapter 6.3.2 --- Syllable-based Language Boundary Detection (SYL LB) --- p.127 / Chapter 6.3.3 --- Language Boundary Detection Based on Syllable Lattice (BILINGUAL LBD) --- p.129 / Chapter 6.3.4 --- Observations --- p.129 / Chapter 6.4 --- Evaluation of the Language Models --- p.130 / Chapter 6.4.1 --- Character Perplexity --- p.130 / Chapter 6.4.2 --- Phonetic-to-text Conversion Rate --- p.131 / Chapter 6.4.3 --- Observations --- p.131 / Chapter 6.5 --- Character Error Rate --- p.132 / Chapter 6.5.1 --- Without Language Boundary Information --- p.133 / Chapter 6.5.2 --- With Language Boundary Detector SYL LBD --- p.134 / Chapter 6.5.3 --- With Language Boundary Detector BILINGUAL-LBD --- p.136 / Chapter 6.5.4 --- Observations --- p.138 / Chapter 6.6 --- References --- p.141 / Chapter Chapter 7 --- Conclusions and Suggestions for Future Work --- p.143 / Chapter 7.1 --- Conclusion --- p.143 / Chapter 7.1.1 --- Difficulties and Solutions --- p.144 / Chapter 7.2 --- Suggestions for Future Work --- p.149 / Chapter 7.2.1 --- Acoustic Modeling --- p.149 / Chapter 7.2.2 --- Pronunciation Modeling --- p.149 / Chapter 7.2.3 --- Language Modeling --- p.150 / Chapter 7.2.4 --- Speech Data --- p.150 / Chapter 7.2.5 --- Language Boundary Detection --- p.151 / Chapter 7.3 --- References --- p.151 / Appendix A Code-mixing Utterances in Training Set of CUMIX --- p.152 / Appendix B Code-mixing Utterances in Testing Set of CUMIX --- p.175 / Appendix C Usage of Speech Data in CUMIX --- p.202

published_or_final_version / Language Studies / Master / Master of Arts

published_or_final_version / Language Studies / Master / Master of Arts

published_or_final_version / abstract / Linguistics / Master / Master of Arts in Applied Linguistics

published_or_final_version / abstract / Linguistics / Master / Master of Arts

published_or_final_version / Linguistics / Master / Master of Arts in Applied Linguistics

M.A., Faculty of Arts, University of the Witwatersrand, 1995

This dissertation studies the problem of identifying the ideological perspective of people as expressed in their written text. One's perspective is often expressed in his/her stance towards polarizing topics. We are interested in studying how nuanced linguistic cues can be used to identify the perspective of a person in informal genres. Moreover, we are interested in exploring the problem from a multilingual perspective comparing and contrasting linguistics devices used in both English informal genres datasets discussing American ideological issues and Arabic discussion fora posts related to Egyptian politics. %In doing so, we solve several challenges. Our first and utmost goal is building computational systems that can successfully identify the perspective from which a given informal text is written while studying what linguistic cues work best for each language and drawing insights into the similarities and differences between the notion of perspective in both studied languages. We build computational systems that can successfully identify the stance of a person in English informal text that deal with different topics that are determined by one's perspective, such as legalization of abortion, feminist movement, gay and gun rights; additionally, we are able to identify a more general notion of perspective–namely the 2012 choice of presidential candidate–as well as build systems for automatically identifying different elements of a person's perspective given an Egyptian discussion forum comment. The systems utilize several lexical and semantic features for both languages. Specifically, for English we explore the use of word sense disambiguation, opinion features, latent and frame semantics as well; as Linguistic Inquiry and Word Count features; in Arabic, however, in addition to using sentiment and latent semantics, we study whether linguistic code-switching (LCS) between the standard and dialectal forms for the language can help as a cue for uncovering the perspective from which a comment was written. This leads us to the challenge of devising computational systems that can handle LCS in Arabic. The Arabic language has a diglossic nature where the standard form of the language (MSA) coexists with the regional dialects (DA) corresponding to the native mother tongue of Arabic speakers in different parts of the Arab world. DA is ubiquitously prevalent in written informal genres and in most cases it is code-switched with MSA. The presence of code-switching degrades the performance of almost any MSA-only trained Natural Language Processing tool when applied to DA or to code-switched MSA-DA content. In order to solve this challenge, we build a state-of-the-art system–AIDA–to computationally handle token and sentence-level code-switching. On a conceptual level, for handling and processing Egyptian ideological perspectives, we note the lack of a taxonomy for the most common perspectives among Egyptians and the lack of corresponding annotated corpora. In solving this challenge, we develop a taxonomy for the most common community perspectives among Egyptians and use an iterative feedback-loop process to devise guidelines on how to successfully annotate a given online discussion forum post with different elements of a person's perspective. Using the proposed taxonomy and annotation guidelines, we annotate a large set of Egyptian discussion fora posts to identify a comment's perspective as conveyed in the priority expressed by the comment, as well as the stance on major political entities.

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