Bangla text normalization for text-to-speech synthesizer using machine learning algorithms
Md. Rezaul Islam, Arif Ahmad, Mohammad Shahidur Rahman
Abstract
Text normalization (TN) for text-to-speech (TTS) synthesizer is the transformation of non-standard words like times, ordinal numbers, equations, ranges, dates, etc. into standard words which have similarities with their pronunciations. An essential part of all TTS synthesizers is text normalization. Without text normalization, generated voice from the TTS synthesizer will be unintelligent. For the unsatisfactory performance of previous research, a text normalization method for the Bangla language is proposed in this paper. At first, we have produced a tokenized data set with a semiotic class using regular expressions from a Bangla corpus. Then each token has been trained using the XGBClassifier algorithm. After that, it identifies the semiotic class for each token of a new Bangla text corpus using the trained XGBClassifier model. Finally, it produced a normalized text for each token by calling the class function according to the predicted class. This text normalization method will help the Bangla TTS synthesizer to produce more intelligent voices. The token classification accuracy of this method is 99.997%.