基于扩散的多通道语音增强自监督学习用于噪声条件下的说话人验证
Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions
AI总结：
本文提出 Diff-Filter 多通道语音增强方法，结合扩散概率模型与两阶段自监督学习，并设计基于等错误率的损失函数，在 MultiSV 数据集上显著提升噪声条件下的说话人验证性能。
摘要：
本文介绍了 Diff-Filter，一种基于扩散概率模型的多通道语音增强方法，用于提升噪声和混响条件下的说话人验证性能。同时提出了一种新的两阶段训练流程，充分利用自监督学习的优势。在第一阶段，通过基于评分的扩散模型进行时域语音滤波来训练 Diff-Filter。在第二阶段，将 Diff-Filter 与预训练的 ECAPA-TDNN 说话人验证模型在自监督学习框架下联合优化。我们提出了一种基于等错误率的新型损失函数，该损失用于在未标注说话人身份的数据集上进行自监督学习。所提方法在多通道说话人验证数据集 MultiSV 上进行了评估，结果表明在噪声多通道条件下性能有显著提升。
英文摘要：
The paper introduces Diff-Filter, a multichannel speech enhancement approach based on the diffusion probabilistic model, for improving speaker verification performance under noisy and reverberant conditions. It also presents a new two-step training procedure that takes the benefit of self-supervised learning. In the first stage, the Diff-Filter is trained by conducting timedomain speech filtering using a scoring-based diffusion model. In the second stage, the Diff-Filter is jointly optimized with a pre-trained ECAPA-TDNN speaker verification model under a self-supervised learning framework. We present a novel loss based on equal error rate. This loss is used to conduct selfsupervised learning on a dataset that is not labelled in terms of speakers. The proposed approach is evaluated on MultiSV, a multichannel speaker verification dataset, and shows significant improvements in performance under noisy multichannel conditions.
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。
