Alex Jin — accepted AI-conference papers

2024

Conformer-Based Speech Recognition On Extreme Edge-Computing Devices

Mingbin Xu, Alex Jin, Sicheng Wang, Mu Su, Tim Ng, Henry Mason +5

NAACL 2024industry

With increasingly more powerful compute capabilities and resources in today’s devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy. However, it is still challenging to implement on-device ASR on resource-…

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2023

Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices

Mingbin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier C. van Dalen +7

ICASSP 2023accepted

Federated Learning (FL) is a technique to train models on distributed edge devices with local data samples. Differential Privacy (DP) can be applied with FL to provide a formal privacy guarantee for sensitive data on device. Our goal is to train a large neural network language model (NNLM) on comput…

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