Self-Incremental Training for Personalized Voice Command Recognition in a Wireless Audio Sensor Network

This paper studies self-incremental training in the context of personalized Deep Neural Networks (DNNs) for voice command recognition tailored for resource-constrained sensor nodes. The learning task runs when new unsupervised data becomes available within a Wireless Audio Sensor Network (WASN). After collecting a new multi-sensor dataset of voice commands, we experimentally investigate network-level policies to assign pseudo-labels to the new data. Our baseline analysis shows an accuracy improvement of up to +15% with respect to models pretrained on a large keyword corpus dataset. The multi-sensor labeling strategy closely approximates the performance achieved in a single-sensor scenario providing a clean signal, while we observe +4.7% compared to other sensors with degraded signal quality.