  Despite the recent advancements in speech recognition, there are still
difficulties in accurately transcribing conversational and emotional speech in
noisy and reverberant acoustic environments. This poses a particular challenge
in the search and rescue (SAR) domain, where transcribing conversations among
rescue team members is crucial to support real-time decision-making. The
scarcity of speech data and associated background noise in SAR scenarios make
it difficult to deploy robust speech recognition systems. To address this
issue, we have created and made publicly available a German speech dataset
called RescueSpeech. This dataset includes real speech recordings from
simulated rescue exercises. Additionally, we have released competitive training
recipes and pre-trained models. Our study highlights that the performance
attained by state-of-the-art methods in this challenging scenario is still far
from reaching an acceptable level.

Audio and Speech Processing;Machine Learning;Sound;Signal Processing