Voice assistants overhear conversations and a consent management mechanism is required. Consent management can be implemented using speaker recognition. Users that do not give consent enrol their voice and all their further recordings are discarded. Building speaker recognition-based consent management is challenging as dynamic registration, removal, and re-registration of speakers must be efficiently handled. This work proposes a consent management system addressing the aforementioned challenges. A contrastive based training is applied to learn the underlying speaker equivariance inductive bias. The contrastive features for buckets of speakers are trained a few steps into each iteration and act as replay buffers. These features are progressively selected using a multi-strided random sampler for classification. Moreover, new methods for dynamic registration using a portion of old utterances, removal, and re-registration of speakers are proposed. The results verify memory efficiency and dynamic capabilities of the proposed methods and outperform the existing approach from the literature.

翻译：语音助手会窃听对话，因此需要一种同意管理机制。同意管理可通过说话人识别来实现。未给予同意的用户需注册其语音，此后所有录音将被丢弃。构建基于说话人识别的同意管理系统面临挑战，因为必须高效处理说话人的动态注册、移除与重新注册。本研究提出了一种应对上述挑战的同意管理系统。采用基于对比学习的训练方法，以学习潜在的说话人等变归纳偏置。每轮迭代中对若干说话人分组的对比特征进行少量步骤训练，并作为回放缓冲区。这些特征通过多步长随机采样器逐步筛选以进行分类。此外，本文提出了使用部分旧语音片段进行动态注册、移除及重新注册说话人的新方法。实验结果验证了所提方法的内存效率与动态能力，并优于现有文献方法。