In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirectional cross-attention mechanism, which facilitates deep contextual fusion between modalities, enabling rich and complementary representation learning. To capture temporal dependencies within the fused embeddings, a stack of lightweight Squeezeformer blocks combining convolutional and attention modules is introduced. The enhanced embeddings are then decoded via a U-Net-style decoder for direct waveform reconstruction, ensuring perceptually consistent and intelligible speech output. Experimental evaluations demonstrate the effectiveness of AUREXA-SE, achieving significant performance improvements over noisy baselines, with STOI of 0.516, PESQ of 1.323, and SI-SDR of -4.322 dB. The source code of AUREXA-SE is available at https://github.com/mtanveer1/AVSEC-4-Challenge-2025.

翻译：本文提出AUREXA-SE（基于交叉注意力与Squeezeformer的视听统一表征交换架构用于语音增强），这是一种专为视听语音增强设计的渐进式双模态框架。AUREXA-SE通过采用基于U-Net的一维卷积编码器处理原始音频波形，以及使用Swin Transformer V2进行高效且富有表现力的视觉特征提取，共同利用原始音频和视觉线索。该架构的核心是一种新颖的双向交叉注意力机制，它促进了模态间的深度上下文融合，实现了丰富且互补的表征学习。为了捕捉融合嵌入中的时序依赖性，本文引入了一组结合卷积与注意力模块的轻量级Squeezeformer块。增强后的嵌入随后通过一个U-Net风格的解码器进行解码，以实现直接波形重建，从而确保感知一致且清晰可懂的语音输出。实验评估证明了AUREXA-SE的有效性，相较于含噪基线取得了显著的性能提升，其STOI为0.516，PESQ为1.323，SI-SDR为-4.322 dB。AUREXA-SE的源代码可在 https://github.com/mtanveer1/AVSEC-4-Challenge-2025 获取。