Unsupervised Speech Enhancement using Data-defined Priors

• URL: http://arxiv.org/abs/2509.22942v1
• Date: Fri, 26 Sep 2025 21:16:08 GMT
• Title: Unsupervised Speech Enhancement using Data-defined Priors
• Authors: Dominik Klement, Matthew Maciejewski, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget,
• Abstract summary: We propose a novel dual-branch encoder-decoder architecture for unsupervised speech enhancement.<n>We show that our method achieves performance comparable to leading unsupervised speech enhancement approaches.
• Score: 15.704587282459315
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The majority of deep learning-based speech enhancement methods require paired clean-noisy speech data. Collecting such data at scale in real-world conditions is infeasible, which has led the community to rely on synthetically generated noisy speech. However, this introduces a gap between the training and testing phases. In this work, we propose a novel dual-branch encoder-decoder architecture for unsupervised speech enhancement that separates the input into clean speech and residual noise. Adversarial training is employed to impose priors on each branch, defined by unpaired datasets of clean speech and, optionally, noise. Experimental results show that our method achieves performance comparable to leading unsupervised speech enhancement approaches. Furthermore, we demonstrate the critical impact of clean speech data selection on enhancement performance. In particular, our findings reveal that performance may appear overly optimistic when in-domain clean speech data are used for prior definition -- a practice adopted in previous unsupervised speech enhancement studies.

Related papers

• Speech Unlearning [14.755831733659699]
  We introduce machine unlearning for speech tasks, a novel and underexplored research problem.<n>It aims to efficiently and effectively remove the influence of specific data from trained speech models without full retraining.<n>It has important applications in privacy preservation, removal of outdated or noisy data, and bias mitigation.
  arXiv  Detail & Related papers  (2025-06-01T06:04:16Z)
• SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech [0.0]
  Speech Enhancement-based Curation Pipeline (SECP) serves as a framework to onboard clean speech. This clean speech can then train a speech enhancement model, which can further refine the original dataset. We show through comparative mean opinion score (CMOS) based subjective tests that the highest and lowest bound of refined data is perceptually better than the original data.
  arXiv  Detail & Related papers  (2024-02-19T19:38:37Z)
• Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning [61.787485727134424]
  We use a state variable to indicate the denoising process. A UNet-like neural network learns to estimate every state variable sampled from the continuous denoising process. Experimental results indicate that preserving a small amount of noise in the clean target benefits speech enhancement.
  arXiv  Detail & Related papers  (2023-09-17T13:27:11Z)
• Adversarial Representation Learning for Robust Privacy Preservation in Audio [11.409577482625053]
  Sound event detection systems may inadvertently reveal sensitive information about users or their surroundings. We propose a novel adversarial training method for learning representations of audio recordings. The proposed method is evaluated against a baseline approach with no privacy measures and a prior adversarial training method.
  arXiv  Detail & Related papers  (2023-04-29T08:39:55Z)
• SPADE: Self-supervised Pretraining for Acoustic DisEntanglement [2.294014185517203]
  We introduce a self-supervised approach to disentangle room acoustics from speech. Our results demonstrate that our proposed approach significantly improves performance over a baseline when labeled training data is scarce.
  arXiv  Detail & Related papers  (2023-02-03T01:36:38Z)
• On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training [33.79711018198589]
  We extend the existing mixture invariant training criterion to exploit both unpaired clean speech and real noisy data. It is found that the unpaired clean speech is crucial to improve quality of separated speech from real noisy speech. The proposed method also performs remixing of processed and unprocessed signals to alleviate the processing artifacts.
  arXiv  Detail & Related papers  (2022-05-03T19:37:58Z)
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis [67.73554826428762]
  We propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech, enabling efficient synthesis of clean, realistic speech from noisy signals.
  arXiv  Detail & Related papers  (2022-03-31T17:57:10Z)
• Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation [60.26511271597065]
  Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models. It is high time that we enhance the robustness of speech processing models to obtain good performance when encountering speech distortions.
  arXiv  Detail & Related papers  (2022-03-30T07:25:52Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)
• PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction [90.55375210094995]
  Speech enhancement aims to improve the perceptual quality of the speech signal by suppression of the background noise. We propose an end-to-end deep learning framework, dubbed PL-EESR, for robust speaker representation extraction.
  arXiv  Detail & Related papers  (2021-10-03T07:05:29Z)
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation [57.68765353264689]
  Speech enhancement and speech separation are two related tasks. Traditionally, these tasks have been tackled using signal processing and machine learning techniques. Deep learning has been exploited to achieve strong performance.
  arXiv  Detail & Related papers  (2020-08-21T17:24:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.