1 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Weikang Wang, Jiajun Zhang, Qian Li +3
Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands a…
Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization
Yangyang Shi, Mei-Yuh Hwang, Xin Lei +1
Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurren…
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model
Yangyang Shi, Mei-Yuh Hwang, Xin Lei
Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficien…
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
He Bai, Yu Zhou, Jiajun Zhang +3
To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. Trans…
Training Augmentation with Adversarial Examples for Robust Speech Recognition
Sining Sun, Ching-Feng Yeh, Mari Ostendorf +2
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast…
Domain Adversarial Training for Accented Speech Recognition
Sining Sun, Ching-Feng Yeh, Mei-Yuh Hwang +2
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source…