  Personal voice activity detection 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 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. 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 and equal error rate.
