Normalization of text messages for text-to-speech

This paper describes a normalization system for text messages to allow them to be read by a TTS engine. To address the large number of texting abbreviations, we use a statistical classifier to learn when to delete a character. The features we use are based on character context, function, and position in the word and containing syllable. To ensure that our system is robust to different abbreviations for a word, we generate multiple abbreviation hypotheses for each word based on the classifier´s prediction. We then reverse the mappings to enable prediction of English words from the abbreviations. Our results show that this approach is feasible and warrants further exploration.