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In the recent years end to end (E2E) automatic speech recognition (ASR)systems have achieved promising results given sufficient resources. Even forlanguages where not a lot of labelled data is available, state of the art E2EASR systems can be developed by pretraining on huge amounts of high resourcelanguages and finetune on low resource languages. For a lot of low resourcelanguages the current approaches are still challenging, since in many caseslabelled data is not available in open domain. In this paper we present anapproach to create labelled data for Maithili, Bhojpuri and Dogri by utilisingpseudo labels from text to speech for forced alignment. The created data wasinspected for quality and then further used to train a transformer basedwav2vec 2.0 ASR model. All data and models are available in open domain.

Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition