Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion

Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel V oice Conversion Sandipan Dhar 1, Md. Email: sandipandhartsk03@gmail.com, tousin@cse.iitb.ac.in, ndjana.cse@nitdgp.ac.in, swagatam.das@isical.ac.in Abstract --After demonstrating significant success in image synthesis, Generative Adversarial Network (GAN) models have likewise made significant progress in the field of speech synthesis, leveraging their capacity to adapt the precise distribution of target data through adversarial learning processes. Notably, in the realm of State-Of-The-Art (SOT A) GAN-based V oice Conversion (VC) models, there exists a substantial disparity in naturalness between real and GAN-generated speech samples. Furthermore, while many GAN models currently operate on a single generator discriminator learning approach, optimizing target data distribution is more effectively achievable through a single generator multi-discriminator learning scheme. Hence, this study introduces a novel GAN model named Collective Learning Mechanism-based Optimal Transport GAN (CLOT - GAN) model, incorporating multiple discriminators, including the Deep Convolutional Neural Network (DCNN) model, Vision Transformer (ViT), and conformer . The objective of integrating various discriminators lies in their ability to comprehend the formant distribution of mel-spectrograms, facilitated by a collective learning mechanism. Simultaneously, the inclusion of Optimal Transport (OT) loss aims to precisely bridge the gap between the source and target data distribution, employing the principles of OT theory.