# Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech

arXiv: https://arxiv.org/abs/2101.08625 · HTML: https://mlsanity.com/paper/2101.08625/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2021.

## Built on by (most cited first, 15 of 15)

- [Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders](https://mlsanity.com/paper/2106.12271/) (2021)
- [RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing](https://mlsanity.com/paper/2202.08862/) (2022)
- [MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech](https://mlsanity.com/paper/2110.05866/) (2021)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [Training Speech Enhancement Systems with Noisy Speech Datasets](https://mlsanity.com/paper/2105.12315/) (2021)
- [Continual self-training with bootstrapped remixing for speech enhancement](https://mlsanity.com/paper/2110.10103/) (2021)
- [Efficient Personalized Speech Enhancement through Self-Supervised Learning](https://mlsanity.com/paper/2104.02017/) (2021)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech](https://mlsanity.com/paper/2210.15368/) (2022)
- [Unsupervised speech enhancement with deep dynamical generative speech and noise models](https://mlsanity.com/paper/2306.07820/) (2023)
- [A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement](https://mlsanity.com/paper/2403.01369/) (2024)
- [Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning](https://mlsanity.com/paper/2304.09552/) (2023)
- [Integrating Uncertainty into Neural Network-based Speech Enhancement](https://mlsanity.com/paper/2305.08744/) (2023)
- [Unsupervised Speech Enhancement using Data-defined Priors](https://mlsanity.com/paper/2509.22942/) (2025)
- [Capacity-Net-Based RIS Precoding Design without Channel Estimation for mmWave MIMO System](https://mlsanity.com/paper/2509.25660/) (2025)

## Abstract (arXiv)

Deep neural network (DNN)-based speech enhancement ordinarily requires clean speech signals as the training target. However, collecting clean signals is very costly because they must be recorded in a studio. This requirement currently restricts the amount of training data for speech enhancement to less than 1/1000 of that of speech recognition which does not need clean signals. Increasing the amount of training data is important for improving the performance, and hence the requirement of clean signals should be relaxed. In this paper, we propose a training strategy that does not require clean signals. The proposed method only utilizes noisy signals for training, which enables us to use a variety of speech signals in the wild. Our experimental results showed that the proposed method can achieve the performance similar to that of a DNN trained with clean signals.