In this paper, we introduce MFCCGAN as a novel speech synthesizer based on
adversarial learning that adopts MFCCs as input and generates raw speech
waveforms. Benefiting the GAN model capabilities, it produces speech with
higher intelligibility than a rule-based MFCC-based speech synthesizer WORLD.
We evaluated the model based on a popular intrusive objective speech
intelligibility measure (STOI) and quality (NISQA score). Experimental results
show that our proposed system outperforms Librosa MFCC- inversion (by an
increase of about 26% up to 53% in STOI and 16% up to 78% in NISQA score) and a
rise of about 10% in intelligibility and about 4% in naturalness in comparison
with conventional rule-based vocoder WORLD that used in the CycleGAN-VC family.
However, WORLD needs additional data like F0. Finally, using perceptual loss in
discriminators based on STOI could improve the quality more. WebMUSHRA-based
subjective tests also show the quality of the proposed approach.