Recent studies have shown promise for designing Brain-Computer Interfaces (BCIs) to restore speech communication for those suffering from neurological injury or disease. Numerous BCIs have been developed to reconstruct different aspects of speech, such as phonemes and words, from brain activity. However, many challenges remain toward the successful reconstruction of continuous speech from brain activity during speech imagery. Here, we investigate the potential of differentiating speech and non-speech using intracranial brain activity in different frequency bands acquired by stereotactic EEG. The results reveal statistically significant information in the alpha and theta bands for detecting voice activity, and that using a combination of multiple frequency bands further improves performance with over 92% accuracy. Furthermore, the model is causal and can be implemented with low-latency for future closed-loop experiments. These preliminary findings show the potential of cross-frequency brain signal features for detecting speech activity to enhance speech decoding and synthesis models.