Unsupervised spoken term discovery is the task of finding recurrent acoustic patterns in speech without any annotations. Current approaches consists of two steps: (1) discovering similar patterns in speech, and (2) partitioning those pairs of acoustic tokens using graph clustering methods. We propose a new approach for the first step. Previous systems used various approximation algorithms to make the search tractable on large amounts of data. Our approach is based on an optimized k-nearest neighbours (KNN) search coupled with a fixed word embedding algorithm. The results show that the KNN algorithm is robust across languages, consistently out-performs the DTW-based baseline, and is competitive with current state-of-the-art spoken term discovery systems.