The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

A method to perform offline and online speaker diarization for an unlimited number of
speakers is described in this paper. End-to-end neural diarization (EEND) has achieved …

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

Conventional methods for speaker diarization involve windowing an audio file into short
segments to extract speaker embeddings, followed by an unsupervised clustering of the …

Speaker diarization is usually referred to as the task that determines``who spoke when''in a
recording. Until a few years ago, all competitive approaches were modular. Systems based …

Speaker diarization, the task of segmenting an audio recording based on speaker identity,
constitutes an important speech pre-processing step for several downstream applications …

Recent works show that speech separation guided diarization (SSGD) is an increasingly
promising direction, mainly thanks to the recent progress in speech separation. It performs …

In this paper, we propose a novel approach for the transcription of speech conversations
with natural speaker overlap, from single channel speech recordings. The proposed model …

In this era of advanced machine intelligence, real-world speech applications need to be
equipped to deal with conversations involving multiple speakers. An essential first step in …

Deep learning has revolutionized speech generation, spanning synthesis areas such as text-
to-speech and voice conversion, leading to diverse advancements. On the one hand, when …