  Deep learning based text-to-speech (TTS) systems have been evolving rapidly
with advances in model architectures, training methodologies, and
generalization across speakers and languages. However, these advances have not
been thoroughly investigated for Indian language speech synthesis. Such
investigation is computationally expensive given the number and diversity of
Indian languages, relatively lower resource availability, and the diverse set
of advances in neural TTS that remain untested. In this paper, we evaluate the
choice of acoustic models, vocoders, supplementary loss functions, training
schedules, and speaker and language diversity for Dravidian and Indo-Aryan
languages. Based on this, we identify monolingual models with FastPitch and
HiFi-GAN V1, trained jointly on male and female speakers to perform the best.
With this setup, we train and evaluate TTS models for 13 languages and find our
models to significantly improve upon existing models in all languages as
measured by mean opinion scores. We open-source all models on the Bhashini
platform.

Computation and Language;Machine Learning;Sound;Audio and Speech Processing