  The ongoing biodiversity crisis, driven by factors such as land-use change
and global warming, emphasizes the need for effective ecological monitoring
methods. Acoustic monitoring of biodiversity has emerged as an important
monitoring tool. Detecting human voices in soundscape monitoring projects is
useful both for analysing human disturbance and for privacy filtering. Despite
significant strides in deep learning in recent years, the deployment of large
neural networks on compact devices poses challenges due to memory and latency
constraints. Our approach focuses on leveraging knowledge distillation
techniques to design efficient, lightweight student models for speech detection
in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to
create compact yet effective student architectures to compare against the
larger EcoVAD teacher model, a well-regarded voice detection architecture in
eco-acoustic monitoring. The comparative analysis included examining various
configurations of the MobileNetV3-Small-Pi derived student models to identify
optimal performance. Additionally, a thorough evaluation of different
distillation techniques was conducted to ascertain the most effective method
for model selection. Our findings revealed that the distilled models exhibited
comparable performance to the EcoVAD teacher model, indicating a promising
approach to overcoming computational barriers for real-time ecological
monitoring.
