Clustering-based Wake Word Detection in Privacy-aware Acoustic Sensor Networks

This work investigates privacy-aware collaborative wake word detection (WWD) in acoustic sensor networks. To meet state-of-the-art privacy constraints, the proposed WWD scheme is based on privacy-aware unsupervised clustered federated learning that groups microphone nodes w.r.t. active sound sources and on a privacy-preserving high-level feature representation. Using the partition of microphone nodes into clusters, we apply intra- and inter-cluster feature enhancement strategies directly in the privacy-preserving feature domain and thus circumvent the need for communicating privacy-sensitive information between nodes. The approach is demonstrated for an acoustic sensor network deployed in a smart-home environment. We show that the proposed collaborative WWD system clearly outperforms independent decisions of individual microphone nodes. Index Terms: privacy, wake word detection, clustering, federated learning, unsupervised clustered federated learning