A Unified Model of Thai Romanization and Word Segmentation

Thai romanization is the way to write Thai language using roman alphabets. It could be performed on the basis of orthographic form (transliteration) or pronunciation (transcription) or both. As a result, many systems of romanization are in use. The Royal Institute has established the standard by proposing the principle of romanization on the basis of transcription. To ensure the standard, a fully automatic Thai romanization system should be publicly made available. In this paper, we discuss the problems of Thai Romanization. We argue that automatic Thai romanization is difficult because the ambiguities of pronunciation are caused not only by the ambiguities of syllable segmentation, but also by the ambiguities of word segmentation. A model of automatic romanization then is designed and implemented on this ground. The problem of romanization and word segmentation are handled simultaneously. A syllable-segmented corpus and a corpus of word-pronunciation are used for training the system. The accuracy of the system is 94.44% for unseen names and 99.58% for general texts. When the training corpus includes some proper names, the accuracy of romanizing unseen names was increased from 94.44% to 97%. Our system performs well because it is designed to better suit the problem

Pronunciation Ambiguities in Japanese Kanji

Japanese writing is a complex system, and a large part of the complexity resides in the use of kanji. A single kanji character in modern Japanese may have multiple pronunciations, either as native vocabulary or as words borrowed from Chinese. This causes a problem for text-to-speech synthesis (TTS) because the system has to predict which pronunciation of each kanji character is appropriate in the context. The problem is called homograph disambiguation. In Japanese TTS technology, the trick in any case is to know which is the right reading, which makes reading Japanese text a challenge. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. The LR classifier improves the modeling performance by 16%. This experiment provides the first experimental research in Japanese single kanji homograph disambiguation. The annotated Japanese data is freely released to the public to support further work

A Context-Sensitive Homograph Disambiguation in Thai Text-to-Speech Synthesis

Homograph ambiguity is an original issue in Text-to-Speech (TTS). To disambiguate homograph, several efficient approaches have been proposed such as part-of-speech (POS) n-gram, Bayesian classifier, decision tree, and Bayesian-hybrid approaches. These methods need words or/and POS tags surrounding the question homographs in disambiguation. Some languages such as Thai, Chinese, and Japanese have no word-boundary delimiter. Therefore before solving homograph ambiguity, we need to identify word boundaries. In this paper, we propose a unique framework that solves both word segmentation and homograph ambiguity problems altogether. Our model employs both local and longdistance contexts, which are automatically extracted by a machine learning technique called Winnow.