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Hidden-Markov-model (HMM) based text-to-speech (HTS) offers flexibility inspeaking styles along with fast training and synthesis while beingcomputationally less intense. HTS performs well even in low-resource scenarios.The primary drawback is that the voice quality is poor compared to that of E2Esystems. A hybrid approach combining HMM-based feature generation andneural-network-based HiFi-GAN vocoder to improve HTS synthesis quality isproposed. HTS is trained on high-resolution mel-spectrograms instead ofconventional mel generalized coefficients (MGC), and the output mel-spectrogramcorresponding to the input text is used in a HiFi-GAN vocoder trained on Indiclanguages, to produce naturalness that is equivalent to that of E2E systems, asevidenced from the DMOS and PC tests.

Fast and small footprint Hybrid HMM-HiFiGAN based system for speech synthesis in Indian languages