# Frustratingly Easy Noise-aware Training of Acoustic Models

arXiv: https://arxiv.org/abs/2011.02090 · HTML: https://mlsanity.com/paper/2011.02090/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps & CHiME-4 Corpora](https://mlsanity.com/paper/2109.11086/) (2021)
- [Speech-enhanced and Noise-aware Networks for Robust Speech Recognition](https://mlsanity.com/paper/2203.13696/) (2022)

## Abstract (arXiv)

Environmental noises and reverberation have a detrimental effect on the performance of automatic speech recognition (ASR) systems. Multi-condition training of neural network-based acoustic models is used to deal with this problem, but it requires many-folds data augmentation, resulting in increased training time. In this paper, we propose utterance-level noise vectors for noise-aware training of acoustic models in hybrid ASR. Our noise vectors are obtained by combining the means of speech frames and silence frames in the utterance, where the speech/silence labels may be obtained from a GMM-HMM model trained for ASR alignments, such that no extra computation is required beyond averaging of feature vectors. We show through experiments on AMI and Aurora-4 that this simple adaptation technique can result in 6-7% relative WER improvement. We implement several embedding-based adaptation baselines proposed in literature, and show that our method outperforms them on both the datasets. Finally, we extend our method to the online ASR setting by using frame-level maximum likelihood for the mean estimation.