Bangla text normalization for text-to-speech synthesizer using machine learning algorithms

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 that 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 unintelligible. 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 dataset 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 in a new Bangla text corpus using the trained XGBClassifier model. Finally, it produces 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 in producing more intelligible voices. The token classification accuracy of this method is 99.997%.

Keywords

• Text-to-speech
• Text normalization
• Machine learning algorithms
• Large Bangla text corpus
• ROC curve
• Confusion matrices