# VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration

arXiv: https://arxiv.org/abs/2204.05841 · HTML: https://mlsanity.com/paper/2204.05841/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2022.

## Built on by (most cited first, 30 of 37)

- [WavJourney: Compositional Audio Creation with Large Language Models](https://mlsanity.com/paper/2307.14335/) (2023)
- [MaskSR: Masked Language Model for Full-band Speech Restoration](https://mlsanity.com/paper/2406.02092/) (2024)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement](https://mlsanity.com/paper/2503.00493/) (2025)
- [AdVerb: Visually Guided Audio Dereverberation](https://mlsanity.com/paper/2308.12370/) (2023)
- [Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement](https://mlsanity.com/paper/2210.17287/) (2022)
- [High-Resolution Speech Restoration with Latent Diffusion Model](https://mlsanity.com/paper/2409.11145/) (2024)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [FINALLY: fast and universal speech enhancement with studio-like quality](https://mlsanity.com/paper/2410.05920/) (2024)
- [Universal Speech Enhancement with Regression and Generative Mamba](https://mlsanity.com/paper/2505.21198/) (2025)
- [Multi-Metric Preference Alignment for Generative Speech Restoration](https://mlsanity.com/paper/2508.17229/) (2025)
- [The IMS Toucan System for the Blizzard Challenge 2023](https://mlsanity.com/paper/2310.17499/) (2023)
- [UniFlow-Audio: Unified Flow Matching for Audio Generation from Omni-Modalities](https://mlsanity.com/paper/2509.24391/) (2025)
- [Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices](https://mlsanity.com/paper/2310.08104/) (2023)
- [Vector Quantized Diffusion Model Based Speech Bandwidth Extension](https://mlsanity.com/paper/2409.05784/) (2024)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Diffusion-based Signal Refiner for Speech Enhancement and Separation](https://mlsanity.com/paper/2305.05857/) (2023)
- [DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech](https://mlsanity.com/paper/2401.08102/) (2024)
- [SPA-SVC: Self-supervised Pitch Augmentation for Singing Voice Conversion](https://mlsanity.com/paper/2406.05692/) (2024)
- [EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training](https://mlsanity.com/paper/2506.16231/) (2025)
- [SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement](https://mlsanity.com/paper/2509.24708/) (2025)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network](https://mlsanity.com/paper/2505.18533/) (2025)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration](https://mlsanity.com/paper/2505.04457/) (2025)
- [CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space](https://mlsanity.com/paper/2603.02022/) (2026)
- [Post-Training Speech Enhancement Language Models with Perceptual Rewards](https://mlsanity.com/paper/2606.21458/) (2026)
- [Speech Separation based on Contrastive Learning and Deep Modularization](https://mlsanity.com/paper/2305.10652/) (2023)

## Abstract (arXiv)

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, speech signals can be degraded by several different distortions simultaneously in the real world. It is thus important to extend speech restoration models to deal with multiple distortions. In this paper, we introduce VoiceFixer, a unified framework for high-fidelity speech restoration. VoiceFixer restores speech from multiple distortions (e.g., noise, reverberation, and clipping) and can expand degraded speech (e.g., noisy speech) with a low bandwidth to 44.1 kHz full-bandwidth high-fidelity speech. We design VoiceFixer based on (1) an analysis stage that predicts intermediate-level features from the degraded speech, and (2) a synthesis stage that generates waveform using a neural vocoder. Both objective and subjective evaluations show that VoiceFixer is effective on severely degraded speech, such as real-world historical speech recordings. Samples of VoiceFixer are available at https://haoheliu.github.io/voicefixer.