93 citations · 104 across the 3 of their papers we have counts for
4 papers
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…
An Empirical Study of Efficient ASR Rescoring with Transformers
Hongzhao Huang, Fuchun Peng
Neural language models (LMs) have been proved to significantly outperform classical n-gram LMs for language modeling due to their superior abilities to model long-range dependencie…
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
Yongqiang Wang, Abdelrahman Mohamed, Duc Le +10
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional emb…
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Hongzhao Huang, Larry Heck, Heng Ji
Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption…