Multi-channel Replay Speech Detection using an Adaptive Learnable Beamformer
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909604092313600 |
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| author | Neri, Michael Virtanen, Tuomas |
| author_facet | Neri, Michael Virtanen, Tuomas |
| contents | Replay attacks belong to the class of severe threats against voice-controlled systems, exploiting the easy accessibility of speech signals by recorded and replayed speech to grant unauthorized access to sensitive data. In this work, we propose a multi-channel neural network architecture called M-ALRAD for the detection of replay attacks based on spatial audio features. This approach integrates a learnable adaptive beamformer with a convolutional recurrent neural network, allowing for joint optimization of spatial filtering and classification. Experiments have been carried out on the ReMASC dataset, which is a state-of-the-art multi-channel replay speech detection dataset encompassing four microphones with diverse array configurations and four environments. Results on the ReMASC dataset show the superiority of the approach compared to the state-of-the-art and yield substantial improvements for challenging acoustic environments. In addition, we demonstrate that our approach is able to better generalize to unseen environments with respect to prior studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-channel Replay Speech Detection using an Adaptive Learnable Beamformer Neri, Michael Virtanen, Tuomas Audio and Speech Processing Signal Processing Replay attacks belong to the class of severe threats against voice-controlled systems, exploiting the easy accessibility of speech signals by recorded and replayed speech to grant unauthorized access to sensitive data. In this work, we propose a multi-channel neural network architecture called M-ALRAD for the detection of replay attacks based on spatial audio features. This approach integrates a learnable adaptive beamformer with a convolutional recurrent neural network, allowing for joint optimization of spatial filtering and classification. Experiments have been carried out on the ReMASC dataset, which is a state-of-the-art multi-channel replay speech detection dataset encompassing four microphones with diverse array configurations and four environments. Results on the ReMASC dataset show the superiority of the approach compared to the state-of-the-art and yield substantial improvements for challenging acoustic environments. In addition, we demonstrate that our approach is able to better generalize to unseen environments with respect to prior studies. |
| title | Multi-channel Replay Speech Detection using an Adaptive Learnable Beamformer |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2502.13473 |