1 citations · 2 across the 5 of their papers we have counts for
5 papers
Are you speaking my languages? On spoken language adherence in multimodal LLMs
Hyungwon Kim, Kandarp Joshi, Lillian Zhou +2
While Large Language Model (LLM) based Automatic Speech Recognition (ASR) enables seamless multilingual use, models often misidentify the output language, compromising transcriptio…
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Lillian Zhou, Yuxin Ding, Mingqing Chen +5
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech. As language evolves and new terms come into use, these models can become ou…
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Dhruv Guliani, Lillian Zhou, Changwan Ryu +5
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents a challenge pertaini…
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training
Lillian Zhou, Dhruv Guliani, Andreas Kabel +2
Transformer-based architectures have been the subject of research aimed at understanding their overparameterization and the non-uniform importance of their layers. Applying these a…
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
Khe Chai Sim, Françoise Beaufays, Arnaud Benard +9
We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ i…