Supervised models for speech enhancement are trained using artificially generated mixtures of clean speech and noise signals. However, the synthetic training conditions may not accurately reflect real-world conditions encountered during testing. This discrepancy can result in poor performance when the test domain significantly differs from the synthetic training domain. To tackle this issue, the UDASE task of the 7th CHiME challenge aimed to leverage real-world noisy speech recordings from the test domain for unsupervised domain adaptation of speech enhancement models. Specifically, this test domain corresponds to the CHiME-5 dataset, characterized by real multi-speaker and conversational speech recordings made in noisy and reverberant domestic environments, for which ground-truth clean speech signals are not available. In this paper, we present the objective and subjective evaluations of the systems that were submitted to the CHiME-7 UDASE task, and we provide an analysis of the results. This analysis reveals a limited correlation between subjective ratings and several supervised nonintrusive performance metrics recently proposed for speech enhancement. Conversely, the results suggest that more traditional intrusive objective metrics can be used for in-domain performance evaluation using the reverberant LibriCHiME-5 dataset developed for the challenge. The subjective evaluation indicates that all systems successfully reduced the background noise, but always at the expense of increased distortion. Out of the four speech enhancement methods evaluated subjectively, only one demonstrated an improvement in overall quality compared to the unprocessed noisy speech, highlighting the difficulty of the task. The tools and audio material created for the CHiME-7 UDASE task are shared with the community.

翻译：监督式语音增强模型使用人工混合的纯净语音和噪声信号进行训练。然而，合成训练条件可能无法准确反映测试时遇到的真实环境条件。当测试域与合成训练域存在显著差异时，这种不匹配会导致模型性能不佳。为解决该问题，第7届CHiME挑战赛的UDASE任务旨在利用来自测试域的真实含噪语音录音，对语音增强模型进行无监督域自适应。具体而言，该测试域对应CHiME-5数据集，其特点是在嘈杂且混响的家庭环境中录制的真实多人对话语音，且缺乏对应的纯净语音真值信号。本文对提交至CHiME-7 UDASE任务的系统进行了主观与客观评估，并对结果进行了分析。分析表明，主观评分与近年来提出的若干监督式非侵入式语音增强性能指标之间存在有限的相关性。相反，研究结果提示，利用为该挑战赛开发的混响LibriCHiME-5数据集进行域内性能评估时，传统侵入式客观指标更为有效。主观评估显示，所有系统均成功降低了背景噪声，但始终以降质加剧为代价。在主观评估的四种语音增强方法中，仅有一种在整体质量上优于未经处理的含噪语音，这凸显了该任务的挑战性。为CHiME-7 UDASE任务创建的工具和音频材料已向社区开源共享。