VoiceFixer: Toward General Speech Restoration with Neural Vocoder
arXiv:2109.13731
Abstract
Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping. However, SSR systems only focus on one task and do not address the general speech restoration problem. In addition, previous SSR systems show limited performance in some speech restoration tasks such as speech super-resolution. To overcome those limitations, we propose a general speech restoration (GSR) task that attempts to remove multiple distortions simultaneously. Furthermore, we propose VoiceFixer, a generative framework to address the GSR task. VoiceFixer consists of an analysis stage and a synthesis stage to mimic the speech analysis and comprehension of the human auditory system. We employ a ResUNet to model the analysis stage and a neural vocoder to model the synthesis stage. We evaluate VoiceFixer with additive noise, room reverberation, low-resolution, and clipping distortions. Our baseline GSR model achieves a 0.499 higher mean opinion score (MOS) than the speech enhancement SSR model. VoiceFixer further surpasses the GSR baseline model on the MOS score by 0.256. Moreover, we observe that VoiceFixer generalizes well to severely degraded real speech recordings, indicating its potential in restoring old movies and historical speeches. The source code is available at https://github.com/haoheliu/voicefixer_main.
References in corpus (9)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
- DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
- Audio Super Resolution using Neural Networks
- On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
- TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
- Joint Echo Cancellation and Noise Suppression based on Cascaded Magnitude and Complex Mask Estimation
- Image to Image Translation based on Convolutional Neural Network Approach for Speech Declipping
- Speech enhancement with weakly labelled data from AudioSet