Search Results for author:
Found 7 papers, 0 papers with code
Understanding Unintended Memorization in Language Models Under Federated Learning
Thus, we initiate a formal study to understand the effect of different components of FL on unintended memorization in trained NWP models.
Mixture-of-Expert Conformer for Streaming Multilingual ASR
We evaluate the proposed model on a set of 12 languages, and achieve an average 11. 9% relative improvement in WER over the baseline.
Efficient Domain Adaptation for Speech Foundation Models
The FM encoder adapter and decoder are then finetuned to the target domain with a small amount of supervised in-domain data.
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework
We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models.
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Personalized Speech recognition on mobile devices
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-time on a Nexus 5 Android smartphone.
