Friends and enemies: a novel initialization for speaker diarization

Abstract

The task of speaker diarization consists of answering the question “Who spoke when?”. The most commonly used approach to speaker diarization is agglomerative clustering of multiple initial clusters. Even though the initial clustering is greatly modified by iterative cluster merging and possibly multiple resegmentations of the data, the initialization algorithm is a key module for system performance and robustness. In this paper we present a novel approach that obtains a desired initial number of clusters in three steps. It first computes possible speaker change points via a standard technique based on the Bayesian information criterion (BIC). It then classifies the resulting segments into ”friend” and ”enemy” groups to finally creates an initial set of clusters for the system. We test this algorithm with the dataset used in the RT05s evaluation, where we show a 13% Diarization error rate relative improvement and a 2.5% absolute cluster purity improvement with respect to our previous algorithm. Index Terms: Speaker diarization, speaker segmentation and clustering, clusters initialization, meetings indexing.