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• W2962760690 abstract "End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recog-nizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories." @default.
• W2962760690 created "2019-07-30" @default.
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• W2962760690 date "2019-05-01" @default.
• W2962760690 modified "2023-10-16" @default.
• W2962760690 title "Streaming End-to-end Speech Recognition for Mobile Devices" @default.
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• W2962760690 doi "https://doi.org/10.1109/icassp.2019.8682336" @default.
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