  Hidden-Markov-model (HMM) based text-to-speech (HTS) offers flexibility in
speaking styles along with fast training and synthesis while being
computationally less intense. HTS performs well even in low-resource scenarios.
The primary drawback is that the voice quality is poor compared to that of E2E
systems. A hybrid approach combining HMM-based feature generation and
neural-network-based HiFi-GAN vocoder to improve HTS synthesis quality is
proposed. HTS is trained on high-resolution mel-spectrograms instead of
conventional mel generalized coefficients (MGC), and the output mel-spectrogram
corresponding to the input text is used in a HiFi-GAN vocoder trained on Indic
languages, to produce naturalness that is equivalent to that of E2E systems, as
evidenced from the DMOS and PC tests.
