Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems. In this paper, we introduce a novel audio-visual SE approach, termed DCUC-Net (deep complex U-Net with conformer network). The proposed DCUC-Net leverages complex domain features and a stack of conformer blocks. The encoder and decoder of DCUC-Net are designed using a complex U-Net-based framework. The audio and visual signals are processed using a complex encoder and a ResNet-18 model, respectively. These processed signals are then fused using the conformer blocks and transformed into enhanced speech waveforms via a complex decoder. The conformer blocks consist of a combination of self-attention mechanisms and convolutional operations, enabling DCUC-Net to effectively capture both global and local audio-visual dependencies. Our experimental results demonstrate the effectiveness of DCUC-Net, as it outperforms the baseline model from the COG-MHEAR AVSE Challenge 2023 by a notable margin of 0.14 in terms of PESQ. Additionally, the proposed DCUC-Net performs comparably to a state-of-the-art model and outperforms all other compared models on the Taiwan Mandarin speech with video (TMSV) dataset.

翻译：近期研究日益认识到将视觉数据融入语音增强（SE）系统的优势。本文提出一种新型音视频语音增强方法，命名为DCUC-Net（基于Conformer网络的深度复数U-Net）。所提出的DCUC-Net利用复数域特征与一叠Conformer模块，其编码器与解码器基于复数U-Net框架构建。音频和视觉信号分别通过复数编码器与ResNet-18模型处理，随后经Conformer模块融合，并通过复数解码器转换为增强后的语音波形。Conformer模块结合自注意力机制与卷积操作，使DCUC-Net能有效捕获全局与局部的音视频依赖关系。实验结果表明，DCUC-Net在PESQ评分上相较于COG-MHEAR AVSE Challenge 2023基线模型显著提升0.14。此外，本模型在台湾普通话视频数据集（TMSV）上表现与当前最优模型相当，并优于所有其他对比模型。