Despite many recent developments in speaker diarization, it remains a challenge and an active area of research to make diarization robust and effective in real-life scenarios. Well-established clustering-based methods are showing good performance and qualities. However, such systems are built of several independent, separately optimized modules, which may cause non-optimum performance. End-to-end neural speaker diarization (EEND) systems are considered the next stepping stone in pursuing high-performance diarization. Nevertheless, this approach also suffers limitations, such as dealing with long recordings and scenarios with a large (more than four) or unknown number of speakers in the recording. The appearance of EEND with encoder-decoder-based attractors (EEND-EDA) enabled us to deal with recordings that contain a flexible number of speakers thanks to an LSTM-based EDA module. A competitive alternative over the referenced EEND-EDA baseline is the EEND with non-autoregressive attractor (EEND-NAA) estimation, proposed recently by the authors of this article. NAA back-end incorporates k-means clustering as part of the attractor estimation and an attractor refinement module based on a Transformer decoder. However, in our previous work on EEND-NAA, we assumed a known number of speakers, and the experimental evaluation was limited to 2-speaker recordings only. In this article, we describe in detail our recent EEND-NAA approach and propose further improvements to the EEND-NAA architecture, introducing three novel variants of the NAA back-end, which can handle recordings containing speech of a variable and unknown number of speakers. Conducted experiments include simulated mixtures generated using the Switchboard and NIST SRE datasets and real-life recordings from the CALLHOME and DIHARD II datasets. In experimental evaluation, the proposed systems achieve up to 51% relative improvement for the simulated scenario and up to 15% for real recordings over the baseline EEND-EDA.

In this study, we analyze the incorporation of cluster-based modeling into the language recognition systems, in which a single utterance is represented as an embedding, deploying widely used i-vectors and x-vectors. We compare the results obtained with a Cosine Distance Scoring, Gaussian Mixture Model, Logistic Regression, and the Mixture of von Misses-Fisher distributions with the classifiers based on the proposed approach which incorporates cluster-based sub-models. Experimental evaluation is performed on the i-vector embeddings from the NIST 2015 language recognition i-vector machine learning challenge and the x-vector embeddings from the Oriental Language Recognition 2020 Challenge (AP20-OLR). The experimental results clearly show that the proposed approach combined with discriminatively trained Logistic Regression classifier achieves notable improvements over the baseline systems, i.e., those without language sub-models, and that our approach is competitive to other systems reported in the literature.