An efficient algorithm for clustering short spoken utterances

Nowadays, spoken dialogue systems which provide automated customer service at call centers become more prevalent. It is time consuming to determine a set of call types for the dialogue system by analyzing a large volume of unstructured spoken utterances. Traditional hierarchical agglomerative clustering (HAC) algorithm can bootstrap the call types in an unsupervised way, yet the time and space complexities are huge, especially for large data set. Based on our observation that spoken utterances containing less than ten terms are common in the spoken dialogue system, we proposed an efficient HAC algorithm for short utterances. By utilizing the particular properties of short utterances, we significantly reduced both the time and the space complexities of the clustering, algorithm.