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：本文提出了一种适用于网络化物联网音频设备的计算高效且分布式的说话人日志框架。该工作提出了一种联邦学习模型，能够在无需大规模音频训练数据库的情况下识别对话参与者。针对该联邦学习模型，本文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，所提出的日志系统通过使用霍特林T平方统计量和贝叶斯信息准则的无监督分割技术，解决了说话人变换检测问题。该新方法将说话人变换检测偏置在检测到的准静音段附近，从而降低了漏检率与误检率之间的权衡严重性。同时，通过语音段的无监督聚类，减少了逐帧说话人识别带来的计算开销。实验结果表明，所提出的训练方法在非独立同分布语音数据条件下具有有效性。该系统在分割阶段显著降低了误检与漏检率，同时减少了计算开销。提升的准确率与降低的计算成本使该机制适用于分布式物联网音频网络的实时说话人日志任务。