# HiFi-VC: High Quality ASR-Based Voice Conversion

arXiv: https://arxiv.org/abs/2203.16937 · HTML: https://mlsanity.com/paper/2203.16937/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://mlsanity.com/paper/1811.00002/) (2018)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization](https://mlsanity.com/paper/1904.05742/) (2019)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling](https://mlsanity.com/paper/2009.02725/) (2020)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [TTS Skins: Speaker Conversion via ASR](https://mlsanity.com/paper/1904.08983/) (2019)
- [Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers](https://mlsanity.com/paper/1911.11601/) (2019)
- [Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques](https://mlsanity.com/paper/2104.00931/) (2021)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments](https://mlsanity.com/paper/2106.08873/) (2021)
- [StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition](https://mlsanity.com/paper/2108.04395/) (2021)

## Built on by (most cited first, 3 of 3)

- [Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes](https://mlsanity.com/paper/2311.17655/) (2023)
- [RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding](https://mlsanity.com/paper/2506.10289/) (2025)
- [Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters](https://mlsanity.com/paper/2507.04817/) (2025)

## Abstract (arXiv)

The goal of voice conversion (VC) is to convert input voice to match the target speaker's voice while keeping text and prosody intact. VC is usually used in entertainment and speaking-aid systems, as well as applied for speech data generation and augmentation. The development of any-to-any VC systems, which are capable of generating voices unseen during model training, is of particular interest to both researchers and the industry. Despite recent progress, any-to-any conversion quality is still inferior to natural speech. In this work, we propose a new any-to-any voice conversion pipeline. Our approach uses automated speech recognition (ASR) features, pitch tracking, and a state-of-the-art waveform prediction model. According to multiple subjective and objective evaluations, our method outperforms modern baselines in terms of voice quality, similarity and consistency.