## 2 Related Works  
Within-show speaker diarization is a very active field of research in which deep learning approaches have recently reach the performance of more classic methods based on Hierarchical Agglomerative Clustering (HAC) [1], K-Means or Spectral Clustering [11] or variational-bayesian modeling [12]. Recent neural approaches have shown tremendous improvement for audio recordings involving a limited number of speakers [13, 14, 15, 16]; however, the inherent difficulty of speaker permutation, often addressed using a PIT loss (permutation invariant training) does not allow current neural end-to-end systems to perform as well as HAC based approaches when dealing with a large number of speaker per audio file (>7) as explained in [17].  
Literature on active learning for speaker diarization is very sparse and existing approaches are complementary to our work more than competitive. In [18], active learning is used to find the initial number of speaker models in a collection of documents. This  
Figure 1: Life-cycle of a human-assisted cross-show speaker diarization system.  
information is used to perform within-show speaker diarization without involving the human expert anymore. In [19], multi-modal active learning is proposed to process speech segments according to their length to add missing labels, task that is out of the scope of our study. Ref. [5] proposed an active learning framework to apply different types of corrections together with metrics to evaluate the cost of human-computer interactions.  
Unlike the previous cited papers, in our work, one interaction with the human expert can lead to correct a whole cluster of segments (obtained with first of two clustering steps) instead of correcting a single segment only.  
In [20], active learning is used to leverage training data and improve a speaker recognition system similar to the one we use for clustering. Active learning based approaches have been developed for other speech processing tasks including speech recognition [21; 22; 23], language recognition [24], speech activity detection [25] or speech emotion recognition [26] but are not directly applicable to speaker diarization.  
Active learning literature for clustering is much wider [27; 28] but mostly focuses on K-means clustering [29; 30] or spectral clustering [31; 32]. Hierarchical agglomerative clustering, that is used in many speaker diarization systems including our baseline, has also been studied for semi-supervised clustering [33; 34]. Those studies propose to use predefined constraints to modify the clustering tree. In our work, instead of modifying the dendrogram, we propose a dynamic approach to update the threshold used to merge and split the clusters.  
Regarding evaluation of the active learning process, multiple approaches have been proposed [5; 35]. In [5], systems are evaluated by DER together with an estimate of the human work load to correct the hypotheses. We make the choice to use a penalized version of DER described in our previous work [36]. The human correction effort is computed to be in the same unit and thus added to the DER in order to provide a single performance estimator reflecting both the final performance and the cost of interacting with humans.  
Cross-show speaker diarization is often considered as the concatenation of two distinct tasks including within show speaker diarization and speaker linking [37; 38; 39]. A few studies have considered cross-show speaker diarization as a whole [40; 41] while, to our knowledge only [4] considers the case of incremental cross-show speaker diarization.