# ConVoice: Real-Time Zero-Shot Voice Style Transfer with Convolutional Network

arXiv: https://arxiv.org/abs/2005.07815 · HTML: https://mlsanity.com/paper/2005.07815/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 6 of 6)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- ["Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World](https://mlsanity.com/paper/2109.09598/) (2021)
- [Deepfake CAPTCHA: A Method for Preventing Fake Calls](https://mlsanity.com/paper/2301.03064/) (2023)
- [SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines](https://mlsanity.com/paper/2111.03811/) (2021)
- [StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts](https://mlsanity.com/paper/2106.00043/) (2021)

## Abstract (arXiv)

We propose a neural network for zero-shot voice conversion (VC) without any parallel or transcribed data. Our approach uses pre-trained models for automatic speech recognition (ASR) and speaker embedding, obtained from a speaker verification task. Our model is fully convolutional and non-autoregressive except for a small pre-trained recurrent neural network for speaker encoding. ConVoice can convert speech of any length without compromising quality due to its convolutional architecture. Our model has comparable quality to similar state-of-the-art models while being extremely fast.