Papers › Voice Conversion Using Speech-to-Speech Neuro-Style Transfer
Voice Conversion Using Speech-to-Speech Neuro-Style Transfer
Ehab A AlBadawy, Siwei Lyu
An impressionist is the one who tries to mimic other people’s voices and their style of speech. Humans have mastered such a task throughout the years. In this work, we introduce a deep learning-based approach to do voice conversion with speech style transfer across different speakers. In our work, we use a combination of Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) as the main components of our proposed model followed by a WaveNet-based vocoder. We use three objective metrics to evaluate our model using the ASVspoof 2019 for measuring the difficulty of differentiating between human and synthesized samples, content verification for transcription accuracy, and speaker encoding for identity verification. Our results show the efficacy of our proposed model in producing a high quality synthesized speech on Flickr8k audio corpus.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections