XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection (2411.10027v2)

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.

• The paper proposes XLSR-Mamba, a dual-column bidirectional state space model that significantly improves spoofing attack detection in speech processing.
• The model integrates forward and backward processing to capture local and global dependencies while reducing computational complexity compared to Transformers.
• Experimental results show superior performance with an EER of 0.93% on ASVspoof 2021 LA, highlighting its potential for real-time anti-spoofing applications.

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

The paper "XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection" by Yang Xiao and Rohan Kumar Das proposes the XLSR-Mamba model — a novel approach designed to enhance the detection of spoofing attacks in speech processing. Building upon recent advancements in state space models (SSMs) and self-supervised learning (SSL), this research delineates a new method that transcends the limitations of existing models through the integration of dual-column bidirectional architectures.

Research Motivation and Context

Spoofing attacks, often perpetrated using text-to-speech (TTS) or voice conversion (VC) technologies, present formidable challenges to speaker verification systems. The proliferation of these technologies necessitates robust anti-spoofing mechanisms to safeguard voice-based applications. This paper responds to such imperatives by extending the capabilities of the Mamba state space model for effective spoofing detection, capitalizing on its proficiency in analyzing long-length sequences inherent in speech data.

Model Architecture

The paper introduces the "DuaBiMamba" — a dual-column bidirectional architecture composed of bidirectional Mamba blocks that are integrated with self-supervised pre-trained models, specifically the wav2vec 2.0-based XLSR. By utilizing dual columns to process forward and backward sequences separately, DuaBiMamba captures nuanced local and global dependencies critical for distinguishing between bonafide and spoofed speech.

This approach contrasts with traditional Transformer models, which, while powerful, face increased computational complexity and inefficiency in capturing long-range temporal dependencies. The dual-column architecture reduces computational burdens and improves inference efficiency, leading to competitive results, particularly in real-world deployment scenarios.

Experimental Results and Performance

Experiments were conducted across multiple datasets, including ASVspoof 2021 LA and DF, and an "In-the-Wild" dataset, which reflects more authentic conditions encountered during real-world spoofing attempts. XLSR-Mamba consistently outperforms existing state-of-the-art approaches in equal error rate (EER) and minimum tandem detection cost function (min t-DCF) metrics. Notably, the model demonstrates superior performance with a notable EER of 0.93% on the ASVspoof 2021 LA dataset and maintained an impressive distinction in the "In-the-Wild" dataset, achieving an EER of 6.71%.

Practical and Theoretical Implications

From a practical perspective, the XLSR-Mamba model offers a significant step towards robust and efficient real-time anti-spoofing solutions. By leveraging the XLSR model for feature extraction and the efficient DuaBiMamba architecture for long-sequence handling, the proposed solution offers reduced inference times, crucial for time-sensitive applications.

Theoretically, the introduction of DuaBiMamba prompts potential re-evaluations of the efficacy of SSMs over traditional attention-based mechanisms within the anti-spoofing domain. The dual-column architecture with bidirectional elements utilized in XLSR-Mamba could inspire future work exploring alternatives to the self-attention paradigm in other AI and machine learning tasks beyond speech processing.

Future Directions

Future research could expand upon this model by exploring how other pre-trained SSL models, such as HuBERT, can be integrated with DuaBiMamba to further refine performance. Additionally, adapting the dual-column architecture may benefit other domains requiring long-sequence processing and anti-spoofing capabilities, including biometrics or multimedia forensics.

In summary, the paper makes a notable contribution to the speech processing community by presenting a model that effectively balances accuracy, speed, and computational efficiency. The XLSR-Mamba model represents a promising innovation in the ongoing efforts to enhance the security and reliability of voice-driven systems against spoofing attacks.