Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements

TL;DR

This paper surveys GAN-based voice conversion (VC), examining how generative adversarial networks map source speaker features to target distributions while preserving linguistic content. It organizes VC into parallel/non-parallel, mono-/cross-lingual, intra-/inter-gender, one-to-one, many-to-many, emotional, and pathological categories, and reviews datasets, loss functions, and vocoder choices. Key contributions include a synthesis of major GAN architectures (e.g., CycleGAN-VC, StarGAN-VC and successors) and a critical appraisal of both objective and subjective evaluation metrics across diverse VC tasks. Persistent challenges—training stability, duration/prosody alignment, and data scarcity—are highlighted, with proposed directions such as integrating diffusion models, audio-visual information, and real-time capabilities to push toward more robust, multilingual VC systems. Overall, the review aims to guide researchers and practitioners in designing robust, efficient VC solutions and to identify gaps for future investigation.

Abstract

Voice conversion (VC) stands as a crucial research area in speech synthesis, enabling the transformation of a speaker's vocal characteristics to resemble another while preserving the linguistic content. This technology has broad applications, including automated movie dubbing, speech-to-singing conversion, and assistive devices for pathological speech rehabilitation. With the increasing demand for high-quality and natural-sounding synthetic voices, researchers have developed a wide range of VC techniques. Among these, generative adversarial network (GAN)-based approaches have drawn considerable attention for their powerful feature-mapping capabilities and potential to produce highly realistic speech. Despite notable advancements, challenges such as ensuring training stability, maintaining linguistic consistency, and achieving perceptual naturalness continue to hinder progress in GAN-based VC systems. This systematic review presents a comprehensive analysis of the voice conversion landscape, highlighting key techniques, key challenges, and the transformative impact of GANs in the field. The survey categorizes existing methods, examines technical obstacles, and critically evaluates recent developments in GAN-based VC. By consolidating and synthesizing research findings scattered across the literature, this review provides a structured understanding of the strengths and limitations of different approaches. The significance of this survey lies in its ability to guide future research by identifying existing gaps, proposing potential directions, and offering insights for building more robust and efficient VC systems. Overall, this work serves as an essential resource for researchers, developers, and practitioners aiming to advance the state-of-the-art (SOTA) in voice conversion technology.

Figures (12)

• Figure 1: Schematic overview of the human speech production mechanism
• Figure 2: Overview of the training and synthesis phase of the speech synthesis process
• Figure 3: Categorization of the speech synthesis processes
• Figure 4: Schematic overview of the VC process
• Figure 5: Three-stage pipeline of the VC process