Development of a Web-Based Service to Transcribe Between Multiple Orthographies of the Iu Mien Language

Thesis (M.S.)--Indiana University South Bend, 2011.The goal of this study was to explore the use of machine learning techniques in the development of a web-based application that transcribes between multiple orthographies of the same language. To this end, source text files used in the publishing of the Iu Mien Bible translation in 4 scripts were merged into a single textbase that served as a text corpus for this study. All syllables in the corpus were combined into a list of parallel renderings which were subjected to ID3 and neural networks with the back propagation in an attempt to achieve machine learning of transcription between the different Iu Mien orthographies. The most effective set of neural net transcription rules were captured and incorporated into a web-based service where visitors could submit text in one writing system and receive a webpage containing the corresponding text rendered in the other writing systems of this language. Transcriptions that are in excess of 90% correct were achieved between a Roman script and another Roman script or between a non-Roman script and another non-Roman script. Transcriptions between a Roman script and a non-Roman yield output that were only 50% correct. This system is still being tested and improved by linguists and volunteers from various organizations associated with the target community within Thailand, Laos, Vietnam and the USA. This study demonstrates the potential of this approach for developing written materials in languages with multiple scripts. This study also provides useful insights on how this technology might be improved

Statistical parametric speech synthesis for Ibibio

Ibibio is a Nigerian tone language, spoken in the south-east coastal region of Nigeria. Like most African languages, it is resource-limited. This presents a major challenge to conventional approaches to speech synthesis, which typically require the training of numerous predictive models of linguistic features such as the phoneme sequence (i.e., a pronunciation dictionary plus a letter-to-sound model) and prosodic structure (e.g., a phrase break predictor). This training is invariably supervised, requiring a corpus of training data labelled with the linguistic feature to be predicted. In this paper, we investigate what can be achieved in the absence of many of these expensive resources, and also with a limited amount of speech recordings. We employ a statistical parametric method, because this has been found to offer good performance even on small corpora, and because it is able to directly learn the relationship between acoustics and whatever linguistic features are available, potentially mitigating the absence of explicit representations of intermediate linguistic layers such as prosody. We present an evaluation that compares systems that have access to varying degrees of linguistic structure. The simplest system only uses phonetic context (quinphones), and this is compared to systems with access to a richer set of context features, with or without tone marking. It is found that the use of tone marking contributes significantly to the quality of synthetic speech. Future work should therefore address the problem of tone assignment using a dictionary and the building of a prediction module for out-of-vocabulary words. Key words: speech synthesis, Ibibio, low-resource languages, HT

Treebank-based acquisition of Chinese LFG resources for parsing and generation

This thesis describes a treebank-based approach to automatically acquire robust,wide-coverage Lexical-Functional Grammar (LFG) resources for Chinese parsing and generation, which is part of a larger project on the rapid construction of deep, large-scale, constraint-based, multilingual grammatical resources. I present an application-oriented LFG analysis for Chinese core linguistic phenomena and (in cooperation with PARC) develop a gold-standard dependency-bank of Chinese f-structures for evaluation. Based on the Penn Chinese Treebank, I design and implement two architectures for inducing Chinese LFG resources, one annotation-based and the other dependency conversion-based. I then apply the f-structure acquisition algorithm together with external, state-of-the-art parsers to parsing new text into "proto" f-structures. In order to convert "proto" f-structures into "proper" f-structures or deep dependencies, I present a novel Non-Local Dependency (NLD) recovery algorithm using subcategorisation frames and f-structure paths linking antecedents and traces in NLDs extracted from the automatically-built LFG f-structure treebank. Based on the grammars extracted from the f-structure annotated treebank, I develop a PCFG-based chart generator and a new n-gram based pure dependency generator to realise Chinese sentences from LFG f-structures. The work reported in this thesis is the first effort to scale treebank-based, probabilistic Chinese LFG resources from proof-of-concept research to unrestricted, real text. Although this thesis concentrates on Chinese and LFG, many of the methodologies, e.g. the acquisition of predicate-argument structures, NLD resolution and the PCFG- and dependency n-gram-based generation models, are largely language and formalism independent and should generalise to diverse languages as well as to labelled bilexical dependency representations other than LFG

Deep Learning for Automatic Assessment and Feedback of Spoken English

Growing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level

Onsetsu hyoki no kyotsusei ni motozuita Ajia moji nyuryoku intafesu ni kansuru kenkyu

制度:新 ; 報告番号:甲3450号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/10/26 ; 早大学位記番号:新577