Online speaker segmentation and clustering using cross-likelihood ratio calculation with reference criterion selection

This study presents a new online method for speaker segmentation and clustering in real-world environments. It analyses and discusses the difficulties of online speaker diarisation and proposes a new segmentation and clustering method, in which the Bayesian information criterion (BIC) and the normalised cross-likelihood ratio (NCLR) are combined into an online speaker diarisation system. A new decision parameter for BIC and NCLR is proposed using normalisation with reference criterion selection (NRCS), together with a window normalisation technique called window-length compensation (WLC), which normalises the criterion value according to analysed window length. The effectiveness of the proposed system and techniques in comparison to the standard offline speaker diarisation system (mClust) is demonstrated on the Slovenian Broadcast News database (BNSI) and an English Broadcast News database (the HUB-4). The online system presented in this study achieves similar performance to the BIC-based offline approach.