A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915584948568064 |
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| author | Kheir, Yassine El Ritter-Guttierez, Fabian Das, Arnab Polzehl, Tim Möller, Sebastian |
| author_facet | Kheir, Yassine El Ritter-Guttierez, Fabian Das, Arnab Polzehl, Tim Möller, Sebastian |
| contents | Recent synthetic speech detection models typically adapt a pre-trained SSL model via finetuning, which is computationally demanding. Parameter-Efficient Fine-Tuning (PEFT) offers an alternative. However, existing methods lack the specific inductive biases required to model the multi-scale temporal artifacts characteristic of spoofed audio. This paper introduces the Multi-Scale Convolutional Adapter (MultiConvAdapter), a parameter-efficient architecture designed to address this limitation. MultiConvAdapter integrates parallel convolutional modules within the SSL encoder, facilitating the simultaneous learning of discriminative features across multiple temporal resolutions, capturing both short-term artifacts and long-term distortions. With only $3.17$M trainable parameters ($1\%$ of the SSL backbone), MultiConvAdapter substantially reduces the computational burden of adaptation. Evaluations on five public datasets, demonstrate that MultiConvAdapter achieves superior performance compared to full fine-tuning and established PEFT methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection Kheir, Yassine El Ritter-Guttierez, Fabian Das, Arnab Polzehl, Tim Möller, Sebastian Sound Recent synthetic speech detection models typically adapt a pre-trained SSL model via finetuning, which is computationally demanding. Parameter-Efficient Fine-Tuning (PEFT) offers an alternative. However, existing methods lack the specific inductive biases required to model the multi-scale temporal artifacts characteristic of spoofed audio. This paper introduces the Multi-Scale Convolutional Adapter (MultiConvAdapter), a parameter-efficient architecture designed to address this limitation. MultiConvAdapter integrates parallel convolutional modules within the SSL encoder, facilitating the simultaneous learning of discriminative features across multiple temporal resolutions, capturing both short-term artifacts and long-term distortions. With only $3.17$M trainable parameters ($1\%$ of the SSL backbone), MultiConvAdapter substantially reduces the computational burden of adaptation. Evaluations on five public datasets, demonstrate that MultiConvAdapter achieves superior performance compared to full fine-tuning and established PEFT methods. |
| title | A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection |
| topic | Sound |
| url | https://arxiv.org/abs/2510.24852 |