  We present SpeechStew, a speech recognition model that is trained on a
combination of various publicly available speech recognition datasets: AMI,
Broadcast News, Common Voice, LibriSpeech, Switchboard/Fisher, Tedlium, and
Wall Street Journal. SpeechStew simply mixes all of these datasets together,
without any special re-weighting or re-balancing of the datasets. SpeechStew
achieves SoTA or near SoTA results across a variety of tasks, without the use
of an external language model. Our results include 9.0\% WER on AMI-IHM, 4.7\%
WER on Switchboard, 8.3\% WER on CallHome, and 1.3\% on WSJ, which
significantly outperforms prior work with strong external language models. We
also demonstrate that SpeechStew learns powerful transfer learning
representations. We fine-tune SpeechStew on a noisy low resource speech
dataset, CHiME-6. We achieve 38.9\% WER without a language model, which
compares to 38.6\% WER to a strong HMM baseline with a language model.
