Voice Activity Detection (VAD) and Overlapped Speech Detection (OSD) are key pre-processing tasks for speaker diarization. In the meeting context, it is often easier to capture speech with a distant device. This consideration however leads to severe performance degradation. We study a unified supervised learning framework to solve distant multi-microphone joint VAD and OSD (VAD+OSD). This paper investigates various multi-channel VAD+OSD front-ends that weight and combine incoming channels. We propose three algorithms based on the Self-Attention Channel Combinator (SACC), previously proposed in the literature. Experiments conducted on the AMI meeting corpus exhibit that channel combination approaches bring significant VAD+OSD improvements in the distant speech scenario. Specifically, we explore the use of learned complex combination weights and demonstrate the benefits of such an approach in terms of explainability. Channel combination-based VAD+OSD systems are evaluated on the final back-end task, i.e. speaker diarization, and show significant improvements. Finally, since multi-channel systems are trained given a fixed array configuration, they may fail in generalizing to other array set-ups, e.g. mismatched number of microphones. A channel-number invariant loss is proposed to learn a unique feature representation regardless of the number of available microphones. The evaluation conducted on mismatched array configurations highlights the robustness of this training strategy.

Over the last decade speaker recognition has witnessed significant advances, with successful developments in Factor Analysis (FA) and more recently i-vectors, more than halving the error rates achieved by the classical UBM/GMM approach. However when very short duration utterances are considered, it is known that these improvements are much less. This paper begins with a review of the recent developments of i-vector systems with a focus on short test duration, in the region of 10 seconds or less. Experimental results are then presented showing that error rates rise from approximately 5% to 18% when the test duration is systematically reduced from 30 seconds to just 3 seconds. Interestingly, with the 30 seconds condition the i-vector error rate is in the region of half that of the corresponding UBM/GMM system. Nevertheless, when the test segments are just 3 seconds duration then the error rates of the 2 systems systems are very similar. All experiments relate to the short-short condition of the NIST 2008 SRE, but with the test segment duration systematically reduced.

Nowadays, state-of-the-art speaker diarization and linking systems heavily rely on between-recording variability compensation methods to accurately process large collections of recordings. Variability estimation is performed on consequent training datasets, which must be labeled by speaker. One major problem of such systems is the acoustic mismatch between training and target data that degrades performances. Most of the collections contain lots of speakers speaking in various acoustic conditions. In this paper, we investigate how the imperfect knowledge of those speakers can help improve between-recording variability estimation, to overcome the mismatch issue. We propose a scalable unsupervised adaptation framework for two types of variability compensation. The proposed framework consists in adapting a state-of-the-art diarization and linking system, trained on $out-domain data$, using the data of the collection itself. Experiments in mismatch condition are run on two French Television shows, while the initial training dataset is composed of Radio recordings. Results indicate that the proposed adaptation framework reduces the cross-recording DER of 13% in average for variable collection sizes.

State of the art diarization systems now achieve decent performance but those performances are often not good enough to deploy them without any human supervision. Additionally, most approaches focus on single audio files while many use cases involving multiple recordings with recurrent speakers require the incremental processing of a collection. In this paper, we propose a framework that solicits a human in the loop to correct the clustering by answering simple questions. After defining the nature of the questions for both single file and collection of files, we propose two algorithms to list those questions and associated stopping criteria that are necessary to limit the work load on the human in the loop. Experiments performed on the ALLIES dataset show that a limited interaction with a human expert can lead to considerable improvement of up to 36.5% relative diarization error rate (DER) for single files and 33.29% for a collection.