The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge
(M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech …

We present a single-stage casual waveform-to-waveform multichannel model that can
separate moving sound sources based on their broad spatial locations in a dynamic …

Speaker extraction aims to extract the target speaker's voice from a multi-talker speech
mixture given an auxiliary reference utterance. Recent studies show that speaker extraction …

In this paper, we propose a multi-channel network for simultaneous speech dereverberation,
enhancement and separation (DESNet). To enable gradient propagation and joint …

In this paper, we conduct a comparative study on speaker-attributed automatic speech
recognition (SA-ASR) in the multi-party meeting scenario, a topic with increasing attention in …

The recently proposed serialized output training (SOT) simplifies multi-talker automatic
speech recognition (ASR) by generating speaker transcriptions separated by a special …

Most deep-learning-based multi-channel speech enhancement methods focus on designing
a set of beamforming coefficients, to directly filter the low signal-to-noise ratio signals …

Speech separation has been successfully applied as a frontend processing module of
conversation transcription systems thanks to its ability to handle overlapped speech and its …

Multi-channel speech enhancement aims at extracting the desired speech using a
microphone array, which has many potential applications, such as video conferencing …

Most existing streaming neural-network-based multi-channel speech separation systems
consist of a causal network architecture and an online spatial information extraction module …