– Bayesian HMM clustering of x-vector sequences (VBx) diarization approach, which
clusters x-vectors using a Bayesian hidden Markov model (BHMM) [285], combined
with a ResNet101 (He et al. [176]) 𝑥-vector extractor achieves superior results on
CALLHOME [111], AMI [53] and DIHARD II [472] datasets

– Attentive Aggregation for Speaker Diarization [278]: This approach uses an attention
mechanism to aggregate embeddings from multiple frames and generate speaker
embeddings. The speaker embeddings are then used for clustering to identify speaker
segments.

– End-to-End Speaker Diarization with Self-Attention [145]: This method uses a self-
attention mechanism to capture the correlations between the input frames and gen-
erates embeddings for each frame. The embeddings are then used for clustering to
identify speaker segments.
AReviewofDeepLearningTechniquesforSpeechProcessing