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Unsupervised and self-supervised learning methods have leveraged unlabelleddata to improve the pretrained models. However, these methods needsignificantly large amount of unlabelled data and the computational cost oftraining models with such large amount of data can be prohibitively high. Weaddress this issue by using unlabelled data during finetuning, instead ofpretraining. We propose acoustic model finetuning (FT) using labelled andunlabelled data. The model is jointly trained to learn representations toclassify senones, as well as learn contextual acoustic representations. Ourtraining objective is a combination of cross entropy loss, suitable forclassification task, and contrastive loss, suitable to learn acousticrepresentations. The proposed approach outperforms conventional finetuning with11.2% and 9.19% word error rate relative (WERR) reduction on Gujarati andBengali languages respectively.

WavFT: Acoustic model finetuning with labelled and unlabelled data