167 citations · 259 across the 14 of their papers we have counts for
17 papers
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion
Zhouyuan Huo, Khe Chai Sim, Bo Li +3
Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks…
JOIST: A Joint Speech and Text Streaming Model For ASR
Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna +6
We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous wo…
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device
Zhouyuan Huo, Dongseong Hwang, Khe Chai Sim +5
Streaming end-to-end speech recognition models have been widely applied to mobile devices and show significant improvement in efficiency. These models are typically trained on the…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
On Large-Cohort Training for Federated Learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo +2
Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each…
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
Bin Gu, An Xu, Zhouyuan Huo +2
The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in whi…