Feature Mean Distance Based Speaker Clustering for Short Speech Segments

An algorithm of speaker clustering is proposed based on Feature Mean Distance (FMD) for short speech segments. First, a distance measure, i.e. FMD, is introduced to represent the similarities between two clusters on the level of feature instead of the level of model. Then, two clusters with the minimum of FMDs are iteratively merged until the minimum of FMDs is larger than an adaptive threshold. Experimental results show average 5% improvements in F measure are obtained in comparison with the AHC+BIC based algorithm. In addition, the proposed algorithm is 4.68 times faster than the AHC+BIC based algorithm.