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arxiv 2203.16823 v1 pith:2H7KMY2L submitted 2022-03-31 cs.CL cs.SDeess.AS

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

Anirudh Gupta , Rishabh Gaur , Ankur Dhuriya , Harveen Singh Chadha , Neeraj Chhimwal , Priyanshi Shah , Vivek Raghavan This is my paper
classification cs.CL cs.SDeess.AS
keywords datalanguagesresourcespeechavailablelabelledalignmentdomain
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 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.

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