# Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems

arXiv: https://arxiv.org/abs/2109.05172 · HTML: https://mlsanity.com/paper/2109.05172/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing](https://mlsanity.com/paper/2202.08862/) (2022)
- [Continual self-training with bootstrapped remixing for speech enhancement](https://mlsanity.com/paper/2110.10103/) (2021)
- [Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation](https://mlsanity.com/paper/2204.03232/) (2022)
- [A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement](https://mlsanity.com/paper/2403.01369/) (2024)

## Abstract (arXiv)

Supervised speech enhancement relies on parallel databases of degraded speech signals and their clean reference signals during training. This setting prohibits the use of real-world degraded speech data that may better represent the scenarios where such systems are used. In this paper, we explore methods that enable supervised speech enhancement systems to train on real-world degraded speech data. Specifically, we propose a semi-supervised approach for speech enhancement in which we first train a modified vector-quantized variational autoencoder that solves a source separation task. We then use this trained autoencoder to further train an enhancement network using real-world noisy speech data by computing a triplet-based unsupervised loss function. Experiments show promising results for incorporating real-world data in training speech enhancement systems.