This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. The work proposes a Federated Learning model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings. Moreover, the proposed diarization system solves the problem of speaker change detection via. unsupervised segmentation techniques using Hotelling's t-squared Statistic and Bayesian Information Criterion. In this new approach, speaker change detection is biased around detected quasi-silences, which reduces the severity of the trade-off between the missed detection and false detection rates. Additionally, the computational overhead due to frame-by-frame identification of speakers is reduced via. unsupervised clustering of speech segments. The results demonstrate the effectiveness of the proposed training method in the presence of non-IID speech data. It also shows a considerable improvement in the reduction of false and missed detection at the segmentation stage, while reducing the computational overhead. Improved accuracy and reduced computational cost makes the mechanism suitable for real-time speaker diarization across a distributed IoT audio network.

翻译：本文提出了一种计算高效且分布式的说话人日志框架，适用于网络化的物联网音频设备。该方法构建了一个联邦学习模型，无需大规模音频数据库进行训练即可识别对话中的参与者。针对该联邦学习模型，提出了一种基于说话人嵌入余弦相似度的无监督在线更新机制。此外，本文提出的说话人日志系统通过基于霍特林T平方统计量和贝叶斯信息准则的无监督分割技术，解决了说话人变化检测问题。在该新方法中，说话人变化检测偏向于检测到的准静音时段，从而减轻了漏检率与误检率之间的权衡程度。同时，通过对话语段进行无监督聚类，减少了逐帧识别说话人带来的计算开销。实验结果证明了所提出的训练方法在非独立同分布语音数据下的有效性。在分割阶段，该方法在降低计算开销的同时，显著减少了误检和漏检。准确率的提升与计算成本的降低使该机制适用于分布式物联网音频网络中的实时说话人日志。