Robustifying automatic speech recognition by extracting slowly varying features

• URL: http://arxiv.org/abs/2112.07400v3
• Date: Wed, 06 Nov 2024 11:32:01 GMT
• Title: Robustifying automatic speech recognition by extracting slowly varying features
• Authors: Matías Pizarro, Dorothea Kolossa, Asja Fischer,
• Abstract summary: We propose a defense mechanism against targeted adversarial attacks. We use hybrid ASR models trained on data pre-processed in such a way. Our model shows a performance on clean data similar to the baseline model, while being more than four times more robust.
• Score: 16.74051650034954
• License:
• Abstract: In the past few years, it has been shown that deep learning systems are highly vulnerable under attacks with adversarial examples. Neural-network-based automatic speech recognition (ASR) systems are no exception. Targeted and untargeted attacks can modify an audio input signal in such a way that humans still recognise the same words, while ASR systems are steered to predict a different transcription. In this paper, we propose a defense mechanism against targeted adversarial attacks consisting in removing fast-changing features from the audio signals, either by applying slow feature analysis, a low-pass filter, or both, before feeding the input to the ASR system. We perform an empirical analysis of hybrid ASR models trained on data pre-processed in such a way. While the resulting models perform quite well on benign data, they are significantly more robust against targeted adversarial attacks: Our final, proposed model shows a performance on clean data similar to the baseline model, while being more than four times more robust.

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