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 …

Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …

The CHiME challenges have played a significant role in the development and evaluation of
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …

This paper proposes a token-level serialized output training (t-SOT), a novel framework for
streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming multi …

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

Guided source separation (GSS) is a type of target-speaker extraction method that relies on
pre-computed speaker activities and blind source separation to perform front-end …

We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings
(``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge …

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

We propose a general framework to compute the word error rate (WER) of ASR systems that
process recordings containing multiple speakers at their input and that produce multiple …

This paper presents our recent effort on end-to-end speaker-attributed automatic speech
recognition, which jointly performs speaker counting, speech recognition and speaker …