## 1 Introduction  
Speaker diarization answers the question \"Who speaks when?\" within an audio recording [1, 2]. Being important for audio indexing, it is also a pre-processing step for many speech tasks such as speech recognition, spoken language understanding or speaker recognition. For an audio stream that involves multiple speakers, diarization is usually achieved in two steps: (i) a segmentation of the audio stream into segments involving a single acoustic event (speech from one speaker, silence, noise...); (ii) a clustering that groups segments along the stream when they belong to the same class of event. A last step could be added to name the resulting speakers but this step is out of the scope of this paper. Speaker diarization addresses the case of a single audio recording while it is often required to link the outputs of a speaker diarization system across consecutive audio recordings where speakers might appear in a recurrent manner. This use-case corresponds to the archiving of group meeting recordings or TV and radio shows. We will refer to the first scenario, addressing a single audio recording, as within-show speaker diarization and to the second one, addressing a collection of audio recordings, as cross-show speaker diarization. When the sequence of shows is finite and the time constraint is not strong, cross-show speaker diarization can be done by concatenating all recordings and considering this task as a global process. However, for the case of long meeting series or TV/Radio shows, it is not reasonable to wait for the end of the collection process before processing the data. In this case, we will consider an incremental cross-show speaker diarization task that consists of processing one show after the other as soon as they are collected. Each show is processed and linked to the previous ones to incrementally extend a database of annotations. In this scenario, we will consider that annotations of a show which has been processed will not be modified once archived. Modern diarization systems achieve decent performance depending on the type of data they process [3, 4] but those performances are often not good enough to deploy such systems without any human supervision [5, 6]. Human assisted learning offers a way to achieve better performance by engaging an interaction between the automatic system and a human expert in order to correct or guide the automatic diarization process [7, 8]. Amongst the different modes of human assisted learning, our work focuses on active learning where the automatic system, while processing an incoming sequence of audio recordings, is allowed to ask simple questions to the human expert [9]. In a previous work, we proposed a human-assisted correction process for within-show speaker diarization [10]. Here, we provide more analyses on this work and extend this approach by considering the case of incremental cross-show speaker diarization also including a human active-correction process. We propose in this study a system architecture depicted in Figure 1. Given a current show and an history of already processed shows, the human assisted speaker diarization (HASD) system first produces an hypothesis based on which a questioning module sends a request to the human expert. The expert's answer is taken into account to correct the current hypothesis and possibly adapt the diarization system. This process iterates until reaching a stopping criteria out of those three: (i) the system has no more questions (ii) the human expert stops answering, or (iii) a maximum interaction cost is reached. In this work, we define a binary question that allows a user/system interaction. For the stage of within-show diarization, we propose two questioning methods with the associated correction module and one method for the stage of cross-show diarization. The scope of this paper does not encompass the integration of both stages nor the system adaptation that will be studied in a future work. Section 2 describes the related works.