Text Normalization using Memory Augmented Neural Networks


We perform text normalization, i.e. the transformation of words from the written to the spoken form, using a memory augmented neural network. With the addition of dynamic memory access and storage mechanism, we present a neural architecture that will serve as a language-agnostic text normalization system while avoiding the kind of unacceptable errors made by the LSTM based recurrent neural networks. By reducing the number of unacceptable mistakes, we show that such a novel architecture is indeed a better alternative. Our proposed system requires significantly lesser amounts of data, training time and compute resources. Although a few occurrences of these errors still remain in certain semiotic classes, we demonstrate that memory augmented networks with meta-learning capabilities can open many doors to a superior text normalization system.

Speech Communication (EURASIP & ISCA) Elsevier