Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural
networks can accurately capture speaker discriminative characteristics and popular deep …

The multiple kernel-means (MKKM) and its variants utilize complementary information from
different sources, achieving better performance than kernel-means (KKM). However, the …

Automatic speaker diarization techniques typically involve a two-stage processing approach
where audio segments of fixed duration are converted to vector representations in the first …

Deep speaker embedding extraction models have recently served as the cornerstone for
modular speaker diarization systems. However, in current modular systems, the extracted …

The performance of most speaker diarization systems with x-vector embeddings is both
vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker …

Speaker diarization relies on the assumption that speech segments corresponding to a
particular speaker are concentrated in a specific region of the speaker space; a region which …

Significant progress has recently been made in speaker diarisation after the introduction of d-
vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for …

Predicting the speaker's identity of short speech segments in human dialogue has been
considered one of the most challenging problems in speech signal processing. Speaker …