  We propose a novel Patched Multi-Condition Training (pMCT) method for robust
Automatic Speech Recognition (ASR). pMCT employs Multi-condition Audio
Modification and Patching (MAMP) via mixing {\it patches} of the same utterance
extracted from clean and distorted speech. Training using patch-modified
signals improves robustness of models in noisy reverberant scenarios. Our
proposed pMCT is evaluated on the LibriSpeech dataset showing improvement over
using vanilla Multi-Condition Training (MCT). For analyses on robust ASR, we
employed pMCT on the VOiCES dataset which is a noisy reverberant dataset
created using utterances from LibriSpeech. In the analyses, pMCT achieves 23.1%
relative WER reduction compared to the MCT.
