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Automatic speech recognition (ASR) systems play a key role in many commercialproducts including voice assistants. Typically, they require large amounts ofclean speech data for training which gives an undue advantage to largeorganizations which have tons of private data. In this paper, we have firstcurated a fairly big dataset using publicly available data sources. Thereafter,we tried to investigate if we can use publicly available noisy data to trainrobust ASR systems. We have used speech enhancement to clean the noisy datafirst 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 ratethan training on just noisy data and 9\% better than training on just cleandata. It's performance is also comparable to the ideal case scenario whentrained on noisy and its clean version.

Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?