Contextual biasing to a specific domain, including a user's song names, app names and contact names, is an important component of any production-level automatic speech recognition (ASR) system. Contextual biasing is particularly challenging in end-toend models because these models keep a small list of candidates during beam search, and also do poorly on proper nouns, which is the main source of biasing phrases. In this paper, we present various algorithmic and training improvements to shallow-fusionbased biasing for end-to-end models. We will show that the proposed approach obtains better performance than a state-ofthe-art conventional model across a variety of tasks, the first time this has been demonstrated.

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Automatic speech recognition (ASR) systems often have difficulty recognizing long-tail entities such as contact names and local restaurant names, which usually do not occur, or occur infrequently, in the system's training data. In this work, we present a method which uses learned text embeddings and nearest neighbor retrieval within a large database of entity embeddings to correct misrecognitions. Our text embeddings are produced by a neural network trained so that the embeddings of acoustically confusable phrases have low cosine distances. Given the embedding of the text of a potential entity misrecognition and a precomputed database containing entities and their corresponding embeddings, we use fast, scalable nearest neighbor retrieval algorithms to find candidate corrections within the database. The inserted candidates are then scored using a function of the original text's cost in the lattice and the distance between the embedding of the original text and the embedding of the candidate correction. Using this lattice augmentation techique, we demonstrate a 46% reduction in word error rate (WER) and 46% reduction in oracle word error rate (OWER) on an evaluation set with popular film queries.