XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection

TL;DR

This paper introduces XLSR-Mamba, a dual-column state space model combined with self-supervised learning, to effectively detect spoofing attacks in speech, achieving high accuracy and efficiency on multiple datasets.

Contribution

It proposes a novel dual-column Mamba architecture integrated with self-supervised learning for improved spoofing detection.

Findings

Achieves competitive results on ASVspoof 2021 datasets.

Faster inference compared to existing methods.

Outperforms in challenging In-the-Wild dataset.

Abstract

Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we apply Mamba for spoofing attack detection. Mamba is well-suited for this task as it can capture the artifacts in spoofed speech signals by handling long-length sequences. However, Mamba's performance may suffer when it is trained with limited labeled data. To mitigate this, we propose combining a new structure of Mamba based on a dual-column architecture with self-supervised learning, using the pre-trained wav2vec 2.0 model. The experiments show that our proposed approach achieves competitive results and faster inference on the ASVspoof 2021 LA and DF datasets, and on the more challenging In-the-Wild dataset, it emerges as the strongest candidate for spoofing attack detection. The code has been publicly released in https://github.com/swagshaw/XLSR-Mamba.