  In this article, we introduce a set of methods to naturalize text based on
natural human speech. Voice-based interactions provide a natural way of
interfacing with electronic systems and are seeing a widespread adaptation of
late. These computerized voices can be naturalized to some degree by inserting
pauses and filler words at appropriate positions. The first proposed text
transformation method uses the frequency of bigrams in the training data to
make appropriate insertions in the input sentence. It uses a probability
distribution to choose the insertions from a set of all possible insertions.
This method is fast and can be included before a Text-To-Speech module. The
second method uses a Recurrent Neural Network to predict the next word to be
inserted. It confirms the insertions given by the bigram method. Additionally,
the degree of naturalization can be controlled in both these methods. On the
conduction of a blind survey, we conclude that the output of these text
transformation methods is comparable to natural speech.
