The Densor is an intraoral sensor platform created to capture unique data from inside the human mouth. This thesis studies the possibility of using Densor-recorded sensor data for Voice Activity Detection (VAD) and basic non-acoustic speech (keyword) recognition. It is part of a broader effort to explore the different use cases of the Densor. This thesis summarizes the performance of existing non-acoustic wearable speech recognition devices, describes the available Densor data, details feature extraction and selection, and shows how Hidden Markov Models can beusedfor both VADandkeyword recognition. The results are promising, with an F1-Score of up to 0.73 for VAD and up to 75% accuracy for keyword recognition. While these results show that both tasks are possible, they cannot be generalized due to the small and unvaried dataset. Future work suggestions include expanding and increasing the variety of the dataset and exploring alternative models such as Conditional Random Fields and Recurrent Neural Networks.