40 citations · 97 across the 6 of their papers we have counts for
6 papers
PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition
Chengxi Lei, Satwinder Singh, Feng Hou +2
Most of the current speech data augmentation methods operate on either the raw waveform or the amplitude spectrum of speech. In this paper, we propose a novel speech data augmentat…
A Novel Self-training Approach for Low-resource Speech Recognition
Satwinder Singh, Feng Hou, Ruili Wang
In this paper, we propose a self-training approach for automatic speech recognition (ASR) for low-resource settings. While self-training approaches have been extensively developed…
How to Design Translation Prompts for ChatGPT: An Empirical Study
Yuan Gao, Ruili Wang, Feng Hou
The recently released ChatGPT has demonstrated surprising abilities in natural language understanding and natural language generation. Machine translation relies heavily on the abi…
Improved Meta Learning for Low Resource Speech Recognition
Satwinder Singh, Ruili Wang, Feng Hou
We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet…
Improving Entity Linking through Semantic Reinforced Entity Embeddings
Feng Hou, Ruili Wang, Jun He +1
Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity…
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Shi Yin, Yi Zhou, Chenguang Li +4
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet,…