Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

This paper investigates an end-to-end neural diarization (EEND) method for an unknown
number of speakers. In contrast to the conventional cascaded approach to speaker …

Speaker diarization is a key component of systems that support multiparty interactions of co-
located users, such as meeting facilitation robots. The goal is to identify who spoke what …

In this paper, we introduce DiarizationLM, a framework to leverage large language models
(LLM) to post-process the outputs from a speaker diarization system. Various goals can be …

Recently, an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR)
model was proposed as a joint model of speaker counting, speech recognition and speaker …

This paper presents Transcribe-to-Diarize, a new approach for neural speaker diarization
that uses an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) …

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

Speaker diarization (SD) is typically used with an automatic speech recognition (ASR)
system to ascribe speaker labels to recognized words. The conventional approach …

We present a Conformer-based end-to-end neural diarization (EEND) model that uses both
acoustic input and features derived from an automatic speech recognition (ASR) model. Two …

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