research
∙
10/13/2022
Mitigating Unintended Memorization in Language Models via Alternating Teaching
Recent research has shown that language models have a tendency to memori...
share
research
∙
10/04/2022
Group Personalized Federated Learning
Federated learning (FL) can help promote data privacy by training a shar...
research
∙
09/07/2022
Modeling Dependent Structure for Utterances in ASR Evaluation
The bootstrap resampling method has been popular for performing signific...
research
∙
01/28/2022
Neural-FST Class Language Model for End-to-End Speech Recognition
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech...
research
∙
09/28/2021
Private Language Model Adaptation for Speech Recognition
Speech model adaptation is crucial to handle the discrepancy between ser...
research
∙
09/19/2021
Model-Based Approach for Measuring the Fairness in ASR
The issue of fairness arises when the automatic speech recognition (ASR)...
research
∙
07/09/2021
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Hybrid automatic speech recognition (ASR) models are typically sequentia...
research
∙
12/01/2020
Federated Marginal Personalization for ASR Rescoring
We introduce federated marginal personalization (FMP), a novel method fo...
research
∙
11/09/2020
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
In this work, to measure the accuracy and efficiency for a latency-contr...
research
∙
11/22/2019
Improving N-gram Language Models with Pre-trained Deep Transformer
Although n-gram language models (LMs) have been outperformed by the stat...
research
∙
10/27/2019
Training ASR models by Generation of Contextual Information
Supervised ASR models have reached unprecedented levels of accuracy, tha...
research
∙
10/24/2019
An Empirical Study of Efficient ASR Rescoring with Transformers
Neural language models (LMs) have been proved to significantly outperfor...
research
∙
10/23/2019
Efficient Dynamic WFST Decoding for Personalized Language Models
We propose a two-layer cache mechanism to speed up dynamic WFST decoding...
research
∙
10/16/2019
Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models
In this work, we study how the large-scale pretrain-finetune framework c...
research
∙
10/19/2012
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
We present a new statistical learning paradigm for Boltzmann machines ba...
research
∙
07/11/2012