An Investigation of Convolution Attention Based Models for Multilingual Speech Synthesis of Indian Languages

In this paper we investigate multi-speaker, multi-lingual speech synthesis for 4 Indic languages (Hindi, Marathi, Gujarathi, Bengali) as well as English in a fully convolutional attention based model. We show how factored embeddings can allow cross lingual transfer and investigate methods to adapt the model in a low resource scenario for the case of Marathi and Gujarati. We also show results on how effectively the model scales to a new language and how much data is required to train the system on a new language.