# pMCT: Patched Multi-Condition Training for Robust Speech Recognition

arXiv: https://arxiv.org/abs/2207.04949 · HTML: https://mlsanity.com/paper/2207.04949/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation](https://mlsanity.com/paper/2605.19833/) (2026)
- [Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training](https://mlsanity.com/paper/2307.12498/) (2023)
- [Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment](https://mlsanity.com/paper/2407.17716/) (2024)

## Abstract (arXiv)

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.