  In the recent years end to end (E2E) automatic speech recognition (ASR)
systems have achieved promising results given sufficient resources. Even for
languages where not a lot of labelled data is available, state of the art E2E
ASR systems can be developed by pretraining on huge amounts of high resource
languages and finetune on low resource languages. For a lot of low resource
languages the current approaches are still challenging, since in many cases
labelled data is not available in open domain. In this paper we present an
approach to create labelled data for Maithili, Bhojpuri and Dogri by utilising
pseudo labels from text to speech for forced alignment. The created data was
inspected for quality and then further used to train a transformer based
wav2vec 2.0 ASR model. All data and models are available in open domain.
