18 citations · 32 across the 17 of their papers we have counts for
17 papers
Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation
Zhe Liu
Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness…
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble
Zhe Liu, Ozlem Kalinli
Recent research has shown that language models have a tendency to memorize rare or unique token sequences in the training corpus. After deploying a model, practitioners might be as…
Contextual Biasing of Named-Entities with Large Language Models
Chuanneng Sun, Zeeshan Ahmed, Yingyi Ma +4
This paper studies contextual biasing with Large Language Models (LLMs), where during second-pass rescoring additional contextual information is provided to a LLM to boost Automati…
Recovering from Privacy-Preserving Masking with Large Language Models
Arpita Vats, Zhe Liu, Peng Su +5
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typica…
Adaptive Multi-Corpora Language Model Training for Speech Recognition
Yingyi Ma, Zhe Liu, Xuedong Zhang
Neural network language model (NNLM) plays an essential role in automatic speech recognition (ASR) systems, especially in adaptation tasks when text-only data is available. In prac…
Mitigating Unintended Memorization in Language Models via Alternating Teaching
Zhe Liu, Xuedong Zhang, Fuchun Peng
Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data. W…
Group Personalized Federated Learning
Zhe Liu, Yue Hui, Fuchun Peng
Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of highly heterogene…
Modeling Dependent Structure for Utterances in ASR Evaluation
Zhe Liu, Fuchun Peng
The bootstrap resampling method has been popular for performing significance analysis on word error rate (WER) in automatic speech recognition (ASR) evaluation. To deal with depend…
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
Neural-FST Class Language Model for End-to-End Speech Recognition
Antoine Bruguier, Duc Le, Rohit Prabhavalkar +7
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transduce…
Private Language Model Adaptation for Speech Recognition
Zhe Liu, Ke Li, Shreyan Bakshi +1
Speech model adaptation is crucial to handle the discrepancy between server-side proxy training data and actual data received on local devices of users. With the use of federated l…
Model-Based Approach for Measuring the Fairness in ASR
Zhe Liu, Irina-Elena Veliche, Fuchun Peng
The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population. In any fairness measurement studie…
Federated Marginal Personalization for ASR Rescoring
Zhe Liu, Fuchun Peng
We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federat…
Statistical Testing on ASR Performance via Blockwise Bootstrap
Zhe Liu, Fuchun Peng
A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where s…
Improving N-gram Language Models with Pre-trained Deep Transformer
Yiren Wang, Hongzhao Huang, Zhe Liu +4
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in infe…
Learning Nonparametric Forest Graphical Models with Prior Information
Yuancheng Zhu, Zhe Liu, Siqi Sun
We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a for…
Graphical Exponential Screening
Zhe Liu
In high dimensions we propose and analyze an aggregation estimator of the precision matrix for Gaussian graphical models. This estimator, called graphical Exponential Screening (gE…