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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)Deploying GOOG-411: Early lessons in data, measurement, and testing., , , , and . ICASSP, page 5260-5263. IEEE, (2008)Personalization of End-to-End Speech Recognition on Mobile Devices for Named Entities., , , , , , , , , and 2 other author(s). ASRU, page 23-30. IEEE, (2019)Unsupervised discovery and training of maximally dissimilar cluster models., , and . INTERSPEECH, page 66-69. ISCA, (2010)Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition, , and . (May 2014)cite arxiv:1402.1128.Application of neural networks to load-frequency control in power systems., , and . Neural Networks, 7 (1): 183-194 (1994)On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech., , , , and . CoRR, (2021)Enabling On-Device Training of Speech Recognition Models With Federated Dropout., , , , , , , and . ICASSP, page 8757-8761. IEEE, (2022)Online Model Compression for Federated Learning with Large Models., , , , , and . ICASSP, page 1-5. IEEE, (2023)