111 citations · 389 across the 65 of their papers we have counts for
10 papers · 1 filter
Transfer Learning for Speech and Language Processing
Dong Wang, Thomas Fang Zheng
Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trai…
Max-margin Metric Learning for Speaker Recognition
Lantian Li, Dong Wang, Chao Xing +1
Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition.…
Binary Speaker Embedding
Lantian Li, Dong Wang, Chao Xing +2
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investiga…
Learning from LDA using Deep Neural Networks
Dongxu Zhang, Tianyi Luo, Dong Wang +1
Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference. In spite of its great success, inferring the latent topic distribution with LDA…
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Lantian Li, Yiye Lin, Zhiyong Zhang +1
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker…
Recognize Foreign Low-Frequency Words with Similar Pairs
Xi Ma, Xiaoxi Wang, Dong Wang +1
Low-frequency words place a major challenge for automatic speech recognition (ASR). The probabilities of these words, which are often important name entities, are generally under-e…