In this work, we test two novel methods of using word embeddings to detect lexical semantic change, attempting to overcome limitations associated with conventional approaches to this problem. Using a diachronic corpus spanning over a hundred years, we generate word embeddings for each decade with the intention of evaluating how meaning changes are represented in embeddings for the same word across time. Our approach differs from previous works in this field in that we encode words as probabilistic Gaussian distributions and bimodal probabilistic Gaussian mixtures, rather than conventional word vectors. We provide a discussion and analysis of our results, comparing the approaches we implemented with those used in previous works. We also conducted further analysis on whether additional information regarding the nature of semantic change could be discerned from particular qualities of the embeddings we generated for our experiments. In our results, we find that encoding words as probabilistic Gaussian embeddings can provide an enhanced degree of reliability with regard to detecting lexical semantic change. Furthermore, we are able to represent additional information regarding the nature of such changes through the variance of these embeddings. Encoding words as bimodal Gaussian mixtures however is generally unsuccessful for this task, proving to be not reliable enough at distinguishing between discrete senses to effectively detect and measure such changes. We provide potential explanations for the results we observe, and propose improvements that can be made to our approach to potentially improve performance.