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 …

In this work, we propose deep latent space clustering for speaker diarization using
generative adversarial network (GAN) back-projection with the help of an encoder network …

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 …

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 …

Suicide is a major societal challenge globally, with a wide range of risk factors, from
individual health, psychological and behavioral elements to socio-economic aspects …

The objective of this work is speaker diarisation of speech recordingsin the wild'. The ability
to determine speech segments is a crucial part of diarisation systems, accounting for a large …

Roles are one of the most important concepts in understanding and modeling human
behavior. According to social psychology, individuals assume distinct roles in different …

Speaker diarization has become an important field in recent years owing to the growing
demand for conversational artificial intelligence and interactive entertainment systems …