A 3-Component System for Text-to-Speech Synthesis

Text-to-speech (TTS) synthesis is the text normalization from written input form to spoken output form, with applications in intelligent personal assistants (IPAs), voice-activated emails, and message readers for the visually impaired. In this project, we built a 3-component TTS text normalization system with token-to-token Naïve Bayes (NB) classifiers, a token-to-class Support Vector Machine (SVM) classifier, and hand-built grammar rules based on regular expression. The performance of this system reached a development accuracy of 99.36% and a testing accuracy of 98.88%. These results are comparable to those obtained by deep learning approaches. Keywords—Machine Learning; Natural Language Processing; Text Normalization; Support Vector Machine; Naïve Bayes