Editing Development of End-to-End Models (section)

==== Low-Resource and Under-Resourced Linguistic Contexts: ====
Due to the lack of annotated data for training automatic recognition models for marginalized or low-resource dialects of languages, future research efforts may turn to methods for improving automatic recognition performance in the domain of resource-scarce languages. In addition to unsupervised or semi-supervised learning frameworks, this may include the judicious use of transfer learning paradigms for resource-rich languages.<ref>D. Wang, J. Yu, X. Wu, L. Sun, X. Liu and H. Meng, "Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization," 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP), Hong Kong, 2021, pp. 1-5.https://ieeexplore.ieee.org/abstract/document/9362068</ref>