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The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
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We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays. The system predominantly comprises of the following integral modules: the Speaker Diarization Module, Multi-channel Audio Front-End Processing Module, and the ASR Module. These components collectively establish a cascading system, meticulously processing multi-channel and multi-speaker audio input. Moreover, this paper highlights the comprehensive optimization process that significantly enhanced our system's performance. Our team's submission is largely based on NeMo toolkits and will be publicly available.
Forward citations
Cited by 2 Pith papers
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Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models
An LLM conditioned on speaker embeddings and utterance time boundaries jointly transcribes and timestamps overlapping multi-speaker speech.
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The DKU System for Multi-Speaker Automatic Speech Recognition in MLC-SLM Challenge
A challenge system combining speaker diarization, speaker embeddings, and a Qwen2.5 LLM adapter architecture reports 18.08% tcpWER on multilingual multi-speaker ASR, far below the 60.39% baseline.
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