MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning
This work addresses speech synthesis for applications requiring high-quality audio, though it is incremental as it builds on existing GAN and vocoder techniques.
The paper tackles speech synthesis by introducing MFCCGAN, a novel synthesizer using adversarial learning with MFCC inputs to generate raw speech waveforms, resulting in improvements of up to 53% in intelligibility and 78% in quality over baseline methods.
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.