Fast and Robust Speaker Clustering Using the Earth Mover'S Distance and Mixmax Models

Speaker clustering is the task of assigning a unique label to all speech segments in a video uttered by the same speaker. There are two key challenges: processing speed and robustness in the presence of noise. In this paper, we present an approach to significantly improve the processing speed of a hierarchical speaker clustering algorithm by using the earth mover's distance (EMD) as the distance measure. By extending the well-known MIXMAX speaker model such that the EMD can be applied, noise robustness is achieved. Experimental results show that the runtime of the proposed EMD approach decreases by more than a factor of 120 compared to a likelihood ratio based distance measure while the clustering performance remains nearly the same.