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 …

When dealing with overlapped speech, the performance of automatic speech recognition
(ASR) systems substantially degrades as they are designed for single-talker speech. To …

Deep neural network-based systems have significantly improved the performance of
speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often …

Large language models (LLMs) have shown great promise for capturing contextual
information in natural language processing tasks. We propose a novel approach to speaker …

This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR)
model that can recognize``who spoke what''with low latency even when multiple people are …

We propose a modular pipeline for the single-channel separation, recognition, and
diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset. Using a …

This paper presents a novel framework for joint speaker diarization (SD) and automatic
speech recognition (ASR), named SLIDAR (sliding-window diarization-augmented …

End-to-end speech translation (ST) for conversation recordings involves several under-
explored challenges such as speaker diarization (SD) without accurate word time stamps …

The scope of speech enhancement has changed from a monolithic view of single,
independent tasks, to a joint processing of complex conversational speech recordings …

Speaker diarization has gained considerable attention within speech processing research
community. Mainstream speaker diarization rely primarily on speakers' voice characteristics …