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Fast and accurate recurrent neural network acoustic models for speech recognition.

, , , and . INTERSPEECH, page 1468-1472. ISCA, (2015)

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Federated Learning Of Out-Of-Vocabulary Words., , , and . CoRR, (2019)Personalization of End-to-End Speech Recognition on Mobile Devices for Named Entities., , , , , , , , , and 2 other author(s). ASRU, page 23-30. IEEE, (2019)Enabling On-Device Training of Speech Recognition Models with Federated Dropout., , , , , , , and . CoRR, (2021)Training Production Language Models without Memorizing User Data., , , , , and . CoRR, (2020)Deploying GOOG-411: Early lessons in data, measurement, and testing., , , , and . ICASSP, page 5260-5263. IEEE, (2008)Transform-domain adaptive filters: an analytical approach.. IEEE Trans. Signal Process., 43 (2): 422-431 (1995)Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework., , and . CoRR, (2020)Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition., , , , , , and . ICASSP, page 6632-6636. IEEE, (2022)Partial Variable Training for Efficient on-Device Federated Learning., , , and . ICASSP, page 4348-4352. IEEE, (2022)Unsupervised discovery and training of maximally dissimilar cluster models., , and . INTERSPEECH, page 66-69. ISCA, (2010)