  Automatic speech recognition (ASR) systems play a key role in many commercial
products including voice assistants. Typically, they require large amounts of
clean speech data for training which gives an undue advantage to large
organizations which have tons of private data. In this paper, we have first
curated a fairly big dataset using publicly available data sources. Thereafter,
we tried to investigate if we can use publicly available noisy data to train
robust ASR systems. We have used speech enhancement to clean the noisy data
first and then used it together with its cleaned version to train ASR systems.
We have found that using speech enhancement gives 9.5\% better word error rate
than training on just noisy data and 9\% better than training on just clean
data. It's performance is also comparable to the ideal case scenario when
trained on noisy and its clean version.
