Pronunciation Ambiguities in J apanese Kanji (2023)

Abstract: 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. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using the logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. 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.

## data-driven kanji homograph research this chapter introduces the japanese kanji homograph data collecting, labeling, and modeling. although very well respected at the current time, this research determined that the part of speech (pos) method is not compatible with japanese homograph disambiguation. an explanation will be presented at the beginning of the chapter. ## discussion and conclusion this research has been motivated by providing the first annotated japanese single kanji pronunciation data set to solve japanese kanji reading ambiguities. although the pronunciation of japanese kanji characters is a bottleneck on tts performance, it has not been studied seriously due to the lack of reliable publicly available data. at the beginning of chapter 2, this research addressed weaknesses in the use of part of speech (pos) as a mean of japanese kanji homograph disambiguation and expounded on the source and methods of obtaining and processing the data. some japanese characteristics, for instance, rendaku, formality, and geographical features were taken in account when processing data. baseline and logistic regression (lr) classifier were used to examine the data performance. while the baseline can only obtain 67% prediction accuracy, the lr classifier, with the help of the ngram feature extractions, effective statistical analysis and regularization, improved the prediction accuracy to 83%. the following sections provide information about the known limitations of this research and directions for future research.