Deep Shallow Fusion for RNN-T Personalization

The research paper "Deep Shallow Fusion for RNN-T Personalization" discusses methods to improve the accuracy of proper nouns and rare words in end-to-end deep learning models, which are typically hard to personalize. Two key techniques mentioned include subword regularization and grapheme-2-grapheme (G2G) augmentation. Subword regularization involves sampling from a list of n-best outputs during training instead of using the highest probable prediction, reducing overfitting on high-frequency words. G2G can generate alternative spellings with similar pronunciations, improving recognition of rare names when used for decoding. These techniques help enhance the model's ability to predict low-frequency words like proper nouns.