# TTS Skins: Speaker Conversion via ASR

arXiv: https://arxiv.org/abs/1904.08983 · HTML: https://mlsanity.com/paper/1904.08983/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection](https://mlsanity.com/paper/2406.02951/) (2024)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Textless Speech Emotion Conversion using Discrete and Decomposed Representations](https://mlsanity.com/paper/2111.07402/) (2021)
- [Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models](https://mlsanity.com/paper/2206.02246/) (2022)
- [PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset](https://mlsanity.com/paper/2405.08838/) (2024)
- [Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques](https://mlsanity.com/paper/2104.00931/) (2021)
- [High Fidelity Speech Regeneration with Application to Speech Enhancement](https://mlsanity.com/paper/2102.00429/) (2021)
- [Discovery of Single Independent Latent Variable](https://mlsanity.com/paper/2110.05887/) (2021)
- [HiFi-VC: High Quality ASR-Based Voice Conversion](https://mlsanity.com/paper/2203.16937/) (2022)
- [Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach](https://mlsanity.com/paper/2511.19080/) (2025)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)
- [UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception](https://mlsanity.com/paper/2605.31521/) (2026)

## Abstract (arXiv)

We present a fully convolutional wav-to-wav network for converting between speakers' voices, without relying on text. Our network is based on an encoder-decoder architecture, where the encoder is pre-trained for the task of Automatic Speech Recognition, and a multi-speaker waveform decoder is trained to reconstruct the original signal in an autoregressive manner. We train the network on narrated audiobooks, and demonstrate multi-voice TTS in those voices, by converting the voice of a TTS robot.