A Generative Data Augmentation Model for Enhancing Chinese Dialect Pronunciation Prediction

Abstract

Most spoken Chinese dialects lack comprehensive digital pronunciation databases, which are crucial for speech processing tasks. Given complete pronunciation databases for related dialects, one can use supervised learning techniques to predict a Chinese character's pronunciation in a target dialect based on the character's features and its pronunciation in other related dialects. Unfortunately, Chinese dialect pronunciation databases are far from complete. We propose a novel generative model that makes use of both existing dialect pronunciation data plus medieval rime books to discover patterns that exist in multiple dialects. The proposed model can augment missing dialectal pronunciations based on existing dialect pronunciation tables (even if incomplete) and the pronunciation data in rime books. The augmented pronunciation database can then be used in supervised learning settings. We evaluate the prediction accuracy in terms of phonological features, such as tone, initial phoneme, final phoneme, etc. For each character, features are evaluated on the whole, overall pronunciation feature accuracy (OPFA). Our first experimental results show that adding features from dialectal pronunciation data to our baseline rime-book model dramatically improves OPFA using the support vector machine (SVM) model. In the second experiment, we compare the performance of the SVM model using phonological features from closely related dialects with that of the model using phonological features from non-closely related dialects. The experimental results show that using features from closely related dialects results in higher accuracy. In the third experiment, we show that using our proposed data augmentation model to fill in missing data can increase the SVM model's OPFA by up to 7.6%.