Voice Activity Detection (VAD) is the first step in a workflow intended for the automated transcription of Indigenous and low-resource languages. However, VAD’s effectiveness when detecting voices in fieldwork settings remains untested. Fieldwork recordings have very different noise and interference conditions from the datasets that mainstream VAD models have been trained for, and so they might fail when confronted with this type of linguistic data. This paper tests different algorithms using data from two typologically distinct Indigenous languages: Bribri from Costa Rica and Cook Islands Māori from Polynesia. We compare energy-based methods (PyDub), GMM-based methods (WebRTC VAD), and two neural-network based methods (Silero and SpeechBrain) against human-annotated transcriptions. Our results indicate that hybrid architectures like that of SpeechBrain obtain the best results (89% accuracy for Bribri and 94% for Cook Islands Māori). However, no system performed well when tagging non-speech segments, which might indicate a bias towards marking the natural noise in a fieldwork setting as a false-positive for voice. With these findings we hope to inform the selection of VAD tools when implementing ASR workflows.