Reassessing Noise Augmentation Methods in the Context of Adversarial Speech

• URL: http://arxiv.org/abs/2409.01813v3
• Date: Wed, 6 Nov 2024 12:05:55 GMT
• Title: Reassessing Noise Augmentation Methods in the Context of Adversarial Speech
• Authors: Karla Pizzi, Matías Pizarro, Asja Fischer,
• Abstract summary: We investigate if noise-augmented training can concurrently improve adversarial robustness in automatic speech recognition systems. The results demonstrate that noise augmentation not only improves model performance on noisy speech but also the model's robustness to adversarial attacks.
• Score: 12.488332326259469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we investigate if noise-augmented training can concurrently improve adversarial robustness in automatic speech recognition (ASR) systems. We conduct a comparative analysis of the adversarial robustness of four different state-of-the-art ASR architectures, where each of the ASR architectures is trained under three different augmentation conditions: one subject to background noise, speed variations, and reverberations, another subject to speed variations only, and a third without any form of data augmentation. The results demonstrate that noise augmentation not only improves model performance on noisy speech but also the model's robustness to adversarial attacks.

Related papers

• Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation [55.752737615873464]
  This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models. We hope this research provides valuable insights as preliminary work toward developing transcription models that maintain consistent performance across a range of acoustic conditions.
  arXiv  Detail & Related papers  (2024-10-18T02:31:36Z)
• An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples [2.2866551516539726]
  A viable adversarial audio file is produced, then, this is fine-tuned with respect to perceptibility and robustness. We present an integrated algorithm that uses psychoacoustic models and room impulse responses (RIR) in the generation step.
  arXiv  Detail & Related papers  (2023-10-05T06:59:09Z)
• On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition [6.006652562747009]
  We investigate a joint ASR-SER learning approach in a low-resource setting. Joint learning can improve ASR word error rate (WER) and SER classification accuracy by 10.7% and 2.3% respectively. Overall, the joint ASR-SER approach yielded more noise-resistant models than the independent ASR and SER approaches.
  arXiv  Detail & Related papers  (2023-05-21T18:52:21Z)
• Inference and Denoise: Causal Inference-based Neural Speech Enhancement [83.4641575757706]
  This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. The proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE.
  arXiv  Detail & Related papers  (2022-11-02T15:03:50Z)
• 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)
• Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps & CHiME-4 Corpora [70.46867541361982]
  We consider a general non-semantic speech representation, which is trained with a self-supervised criteria based on triplet loss called TRILL. We observe +5.42% and +3.18% relative WER improvement for the development and evaluation sets of Fearless Steps.
  arXiv  Detail & Related papers  (2021-09-23T00:43:32Z)
• An Investigation of End-to-End Models for Robust Speech Recognition [20.998349142078805]
  We present a comparison of speech enhancement-based techniques and three different model-based adaptation techniques for robust automatic speech recognition. While adversarial learning is the best-performing technique on certain noise types, it comes at the cost of degrading clean speech WER. On other relatively stationary noise types, a new speech enhancement technique outperformed all the model-based adaptation techniques.
  arXiv  Detail & Related papers  (2021-02-11T19:47:13Z)
• Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement [102.48582597586233]
  We present a U-Net based attention model, U-Net$_At$, to enhance adversarial speech signals. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks.
  arXiv  Detail & Related papers  (2020-03-31T02:16:34Z)
• Improving noise robust automatic speech recognition with single-channel time-domain enhancement network [100.1041336974175]
  We show that a single-channel time-domain denoising approach can significantly improve ASR performance. We show that single-channel noise reduction can still improve ASR performance.
  arXiv  Detail & Related papers  (2020-03-09T09:36:31Z)

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.