A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the Multi-Scale Convolutional Adapter, a parameter-efficient architecture that significantly improves synthetic speech detection by capturing multi-scale temporal artifacts with a minimal number of trainable parameters. This work represents a meaningful advancement in the field of audio processing, particularly in combating the challenges posed by synthetic speech technologies.
Methodology
The proposed Multi-Scale Convolutional Adapter (MultiConvAdapter) introduces a novel architecture that integrates parallel convolutional modules within a self-supervised learning (SSL) encoder, allowing for the simultaneous learning of features across multiple temporal resolutions. This approach effectively addresses the limitations of existing parameter-efficient fine-tuning (PEFT) methods by incorporating inductive biases tailored for the temporal characteristics of synthetic speech. The methodology is well-structured, leveraging depthwise convolutions to maintain a low parameter count while enhancing the model's ability to capture both short-term and long-term artifacts in spoofed audio.
Results & evaluation
The experiments conducted on five public datasets demonstrate the robustness and effectiveness of MultiConvAdapter compared to full fine-tuning and other PEFT methods. The results show a significant reduction in Equal Error Rate (EER) across various configurations, indicating that the proposed method not only achieves superior performance but also does so with a fraction of the parameters. The comprehensive evaluation across diverse datasets reflects a thorough understanding of the challenges in synthetic speech detection.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the SSL backbone, training parameters, and data augmentation techniques. However, the absence of a publicly available code repository or demo URL limits the reproducibility of the results. Future work should consider sharing the implementation to facilitate validation by the research community.
Limitations
While the MultiConvAdapter shows promising results, the paper does not extensively discuss potential limitations, such as the generalizability of the model across different types of synthetic speech or its performance in real-world scenarios. Additionally, the reliance on specific kernel sizes may not universally apply to all datasets, indicating a need for further exploration of adaptive kernel selection.
Broader impact
The advancements in synthetic speech detection have significant implications for security and misinformation mitigation in the context of AI-generated audio. The proposed method could enhance the reliability of systems designed to distinguish between genuine and spoofed speech, thus contributing positively to the integrity of audio communications in various applications, including media, security, and online interactions. The main contribution of this paper is the introduction of the Multi-Scale Convolutional Adapter, a parameter-efficient architecture that significantly improves synthetic speech detection by capturing multi-scale temporal artifacts with a minimal number of trainable parameters. This work represents a meaningful advancement in the field of audio processing, particularly in combating the challenges posed by synthetic speech technologies.
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