第7届CHiME挑战赛UDASE任务中语音增强方法的客观与主观评估
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
AI总结：
针对合成训练与真实测试域不匹配导致语音增强性能下降的问题，本文对CHiME-7 UDASE任务的参赛系统开展主客观评估，发现主客观指标相关性有限，仅一种方法提升了语音整体质量，并共享相关工具与素材。
摘要：
语音增强的监督模型是利用人工生成的干净语音与噪声信号混合数据训练的。然而，合成训练条件可能无法准确反映测试时遇到的真实场景。当测试域与合成训练域存在显著差异时，这种偏差会导致模型性能下降。为解决这一问题，第7届CHiME挑战赛的UDASE任务旨在利用测试域的真实带噪语音录音，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，其特点是在嘈杂且带混响的家庭环境中录制的真实多说话人对话语音，且没有对应的干净语音真值信号。本文对提交至CHiME-7 UDASE任务的系统进行了客观与主观评估，并对结果展开分析。分析发现，主观评分与近期提出的几种用于语音增强的监督式非侵入性能指标之间的相关性有限。相反，结果表明，借助本次挑战赛开发的带混响LibriCHiME-5数据集，更传统的侵入式客观指标可用于域内性能评估。主观评估显示，所有系统都成功降低了背景噪声，但始终以增加失真为代价。在接受主观评估的四种语音增强方法中，仅有一种相较于未处理的带噪语音在整体质量上有所提升，凸显了该任务的难度。本次为CHiME-7 UDASE任务创建的工具和音频素材已向社区共享。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
