1.1k citations · 2.8k across the 23 of their papers we have counts for
9 papers · 1 filter
Mixture-of-Expert Conformer for Streaming Multilingual ASR
Ke Hu, Bo Li, Tara N. Sainath +2
End-to-end models with large capacity have significantly improved multilingual automatic speech recognition, but their computation cost poses challenges for on-device applications.…
Efficient Domain Adaptation for Speech Foundation Models
Bo Li, Dongseong Hwang, Zhouyuan Huo +8
Foundation models (FMs), that are trained on broad data at scale and are adaptable to a wide range of downstream tasks, have brought large interest in the research community. Benef…
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
Yuan Shangguan, Kate Knister, Yanzhang He +2
The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate. Incremental speech recognizers outp…
Federated Learning of N-gram Language Models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews +4
We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to trai…
Federated Learning Of Out-Of-Vocabulary Words
Mingqing Chen, Rajiv Mathews, Tom Ouyang +1
We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vo…
Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews +6
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual…
Mobile Keyboard Input Decoding with Finite-State Transducers
Tom Ouyang, David Rybach, Françoise Beaufays +1
We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recogniti…
Personalized Speech recognition on mobile devices
Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez +8
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-ti…
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
Haşim Sak, Andrew Senior, Kanishka Rao +1
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech re…