Architecture for Text Normalization using Statistical Machine Translation techniques

This paper proposes an architecture, based on statistical machine translation, for developing the text normalization module of a text to speech conversion system. The main target is to generate a language independent text normalization module, based on data and flexible enough to deal with all situations presented in this task. The proposed architecture is composed by three main modules: a tokenizer module for splitting the text input into a token graph (tokenization), a phrase-based translation module (token translation) and a postprocessing module for removing some tokens. This paper presents initial experiments for numbers and abbreviations. The very good results obtained validate the proposed architecture.