Investigation of transfer learning for ASR using LF-MMI trained neural networks

Abstract

It is common in applications of ASR to have a large amount of data out-of-domain to the test data and a smaller amount of in-domain data similar to the test data. In this paper, we investigate different ways to utilize this out-of-domain data to improve ASR models based on Lattice-free MMI (LF-MMI). In particular, we experiment with multi-task training using a network with shared hidden layers; and we try various ways of adapting previously trained models to a new domain. Both types of methods are effective in reducing the WER versus in-domain models, with the jointly trained models generally giving more improvement.