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We study the effectiveness of several techniques to personalize end-to-endspeech models and improve the recognition of proper names relevant to the user.These techniques differ in the amounts of user effort required to providesupervision, and are evaluated on how they impact speech recognitionperformance. We propose using keyword-dependent precision and recall metrics tomeasure vocabulary acquisition performance. We evaluate the algorithms on adataset that we designed to contain names of persons that are difficult torecognize. Therefore, the baseline recall rate for proper names in this datasetis very low: 2.4%. A data synthesis approach we developed brings it to 48.6%,with no need for speech input from the user. With speech input, if the usercorrects only the names, the name recall rate improves to 64.4%. If the usercorrects all the recognition errors, we achieve the best recall of 73.5%. Toeliminate the need to upload user data and store personalized models on aserver, we focus on performing the entire personalization workflow on a mobiledevice.

Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities