  Modern text-to-speech (TTS) systems use deep learning to synthesize speech
increasingly approaching human quality, but they require a database of high
quality audio-text sentence pairs for training. Malayalam, the official
language of the Indian state of Kerala and spoken by 35+ million people, is a
low resource language in terms of available corpora for TTS systems. In this
paper, we present IMaSC, a Malayalam text and speech corpora containing
approximately 50 hours of recorded speech. With 8 speakers and a total of
34,473 text-audio pairs, IMaSC is larger than every other publicly available
alternative. We evaluated the database by using it to train TTS models for each
speaker based on a modern deep learning architecture. Via subjective
evaluation, we show that our models perform significantly better in terms of
naturalness compared to previous studies and publicly available models, with an
average mean opinion score of 4.50, indicating that the synthesized speech is
close to human quality.
