Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence

Personal voice activity detection (PVAD) has received increased attention due to the growing popularity of personal mobile devices and smart speakers. PVAD is often an integral element to speech enhancement and recognition for these applications in which lightweight signal processing is only enabled for the target user. However, in real-world scenarios, the detection performance may degrade because of competing speakers, background noise, and reverberation. To address this problem, we proposed to use equivalent rectangular bandwidth (ERB)-scaled spatial coherence as the input feature to train an array configuration-agnostic PVAD (ARCA-PVAD) network. Whereas the network model requires only 112k parameters, it exhibits excellent detection performance and robustness in adverse acoustic conditions. Notably, the proposed ARCA-PVAD system is scalable to array configurations (geometry, number of microphones, and spacing). Experimental results have demonstrated the superior performance achieved by the proposed ARCA-PVAD system over a baseline in terms of the area under receiver operating characteristic curve (AUC) and equal error rate (EER).