# Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders

arXiv: https://arxiv.org/abs/1908.02590 · HTML: https://mlsanity.com/paper/1908.02590/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 27 of 27)

- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [A survey of multimodal deep generative models](https://mlsanity.com/paper/2207.02127/) (2022)
- [Visual Acoustic Matching](https://mlsanity.com/paper/2202.06875/) (2022)
- [Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders](https://mlsanity.com/paper/2106.12271/) (2021)
- [FlowAVSE: Efficient Audio-Visual Speech Enhancement with Conditional Flow Matching](https://mlsanity.com/paper/2406.09286/) (2024)
- [MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models](https://mlsanity.com/paper/2406.04673/) (2024)
- [AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model](https://mlsanity.com/paper/2308.07593/) (2023)
- [Audio-Visual Speech Enhancement with Score-Based Generative Models](https://mlsanity.com/paper/2306.01432/) (2023)
- [Diffusion-based Unsupervised Audio-visual Speech Enhancement](https://mlsanity.com/paper/2410.05301/) (2024)
- [Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement](https://mlsanity.com/paper/2102.04144/) (2021)
- [Expression-preserving face frontalization improves visually assisted speech processing](https://mlsanity.com/paper/2204.02810/) (2022)
- [Missingness-resilient Video-enhanced Multimodal Disfluency Detection](https://mlsanity.com/paper/2406.06964/) (2024)
- [The impact of removing head movements on audio-visual speech enhancement](https://mlsanity.com/paper/2202.00538/) (2022)
- [Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement](https://mlsanity.com/paper/2501.13375/) (2025)
- [Variational Structured Attention Networks for Deep Visual Representation Learning](https://mlsanity.com/paper/2103.03510/) (2021)
- [Audio-visual speech enhancement with a deep Kalman filter generative model](https://mlsanity.com/paper/2211.00988/) (2022)
- [Fast and efficient speech enhancement with variational autoencoders](https://mlsanity.com/paper/2211.02728/) (2022)
- [A Survey of Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2412.00049/) (2024)
- [Audio-visual video-to-speech synthesis with synthesized input audio](https://mlsanity.com/paper/2307.16584/) (2023)
- [Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2202.13673/) (2022)
- [A weighted-variance variational autoencoder model for speech enhancement](https://mlsanity.com/paper/2211.00990/) (2022)
- [Integrating Uncertainty into Neural Network-based Speech Enhancement](https://mlsanity.com/paper/2305.08744/) (2023)
- [End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments](https://mlsanity.com/paper/2508.13576/) (2025)
- [Real-Time System for Audio-Visual Target Speech Enhancement](https://mlsanity.com/paper/2509.20741/) (2025)
- [Lightweight Wasserstein Audio-Visual Model for Unified Speech Enhancement and Separation](https://mlsanity.com/paper/2512.06689/) (2025)
- [Visual-Informed Speech Enhancement Using Attention-Based Beamforming](https://mlsanity.com/paper/2603.05270/) (2026)
- [LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2603.13952/) (2026)

## Abstract (arXiv)

Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. One advantage of this generative approach is that it does not require pairs of clean and noisy speech signals at training. In this paper, we propose audio-visual variants of VAEs for single-channel and speaker-independent speech enhancement. We develop a conditional VAE (CVAE) where the audio speech generative process is conditioned on visual information of the lip region. At test time, the audio-visual speech generative model is combined with a noise model based on nonnegative matrix factorization, and speech enhancement relies on a Monte Carlo expectation-maximization algorithm. Experiments are conducted with the recently published NTCD-TIMIT dataset as well as the GRID corpus. The results confirm that the proposed audio-visual CVAE effectively fuses audio and visual information, and it improves the speech enhancement performance compared with the audio-only VAE model, especially when the speech signal is highly corrupted by noise. We also show that the proposed unsupervised audio-visual speech enhancement approach outperforms a state-of-the-art supervised deep learning method.