Real-Time Speech Enhancement via a Hybrid ViT: A Dual-Input Acoustic-Image Feature Fusion

• URL: http://arxiv.org/abs/2511.11825v1
• Date: Fri, 14 Nov 2025 19:27:42 GMT
• Title: Real-Time Speech Enhancement via a Hybrid ViT: A Dual-Input Acoustic-Image Feature Fusion
• Authors: Behnaz Bahmei, Siamak Arzanpour, Elina Birmingham,
• Abstract summary: Speech quality and intelligibility are significantly degraded in noisy environments.<n>This paper presents a novel transformer-based learning framework to address the single-channel noise suppression problem.
• Score: 1.376408511310322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech quality and intelligibility are significantly degraded in noisy environments. This paper presents a novel transformer-based learning framework to address the single-channel noise suppression problem for real-time applications. Although existing deep learning networks have shown remarkable improvements in handling stationary noise, their performance often diminishes in real-world environments characterized by non-stationary noise (e.g., dog barking, baby crying). The proposed dual-input acoustic-image feature fusion using a hybrid ViT framework effectively models both temporal and spectral dependencies in noisy signals. Designed for real-world audio environments, the proposed framework is computationally lightweight and suitable for implementation on embedded devices. To evaluate its effectiveness, four standard and commonly used quality measurements, namely PESQ, STOI, Seg SNR, and LLR, are utilized. Experimental results obtained using the Librispeech dataset as the clean speech source and the UrbanSound8K and Google Audioset datasets as the noise sources, demonstrate that the proposed method significantly improves noise reduction, speech intelligibility, and perceptual quality compared to the noisy input signal, achieving performance close to the clean reference.

Related papers

• SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models [49.313324100819955]
  Signal Embedding Energy (SEE) is a method for quantifying the impact of noise intensity on LALM inputs.<n>SEE exhibits a strong correlation with LALM performance, achieving a correlation of 0.98.<n>This paper introduces a novel metric for noise quantification in LALMs, providing guidance for robustness improvements in real-world deployments.
  arXiv  Detail & Related papers  (2026-01-12T08:57:55Z)
• Real Noise Decoupling for Hyperspectral Image Denoising [14.247569090609828]
  Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs.<n>Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks.<n>We propose a multi-stage noise-decoupling framework that decomposes complex noise into explicitly modeled and implicitly modeled components.
  arXiv  Detail & Related papers  (2025-11-21T12:23:07Z)
• TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition [29.756961194844717]
  The proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments. Results validate that the proposed system substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments.
  arXiv  Detail & Related papers  (2024-04-19T16:09:17Z)
• Robust Active Speaker Detection in Noisy Environments [29.785749048315616]
  We formulate a robust active speaker detection (rASD) problem in noisy environments. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance. We propose a novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features.
  arXiv  Detail & Related papers  (2024-03-27T20:52:30Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• NLIP: Noise-robust Language-Image Pre-training [95.13287735264937]
  We propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. Our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way.
  arXiv  Detail & Related papers  (2022-12-14T08:19:30Z)
• NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling [34.565077865854484]
  We propose noise adaptive speech enhancement with target-conditional resampling (NASTAR) NASTAR uses a feedback mechanism to simulate adaptive training data via a noise extractor and a retrieval model. Experimental results show that NASTAR can effectively use one noisy speech sample to adapt an SE model to a target condition.
  arXiv  Detail & Related papers  (2022-06-18T00:15:48Z)
• Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training [50.018580462619425]
  We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN) PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space. For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
  arXiv  Detail & Related papers  (2022-04-06T14:09:02Z)
• 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)
• Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding [76.89426311082927]
  Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference. We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space. Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
  arXiv  Detail & Related papers  (2021-04-13T17:54:33Z)
• CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application [63.2243126704342]
  This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC) Compared with the noisy speech signals, the enhanced speech signals achieved about 6% and 33% of improvements.
  arXiv  Detail & Related papers  (2020-08-21T02:04:12Z)

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.