D
Dong Yu
Researcher at Tencent
Publications - 389
Citations - 45733
Dong Yu is an academic researcher from Tencent. The author has contributed to research in topics: Artificial neural network & Word error rate. The author has an hindex of 72, co-authored 339 publications receiving 39098 citations. Previous affiliations of Dong Yu include Peking University & Microsoft.
Papers
More filters
Proceedings ArticleDOI
Recurrent deep neural networks for robust speech recognition
TL;DR: Full recurrent connections are added to certain hidden layer of a conventional feedforward DNN and allow the model to capture the temporal dependency in deep representations to achieve state-of-the-art performance without front-end preprocessing, speaker adaptive training or multiple decoding passes.
Proceedings ArticleDOI
High-performance hmm adaptation with joint compensation of additive and convolutive distortions via Vector Taylor Series
TL;DR: A model-domain environment-robust adaptation algorithm, which demonstrates high performance in the standard Aurora 2 speech recognition task and adaptation of the dynamic portion of the HMM mean and variance parameters is critical to the success of the algorithm.
Journal ArticleDOI
The Deep Tensor Neural Network With Applications to Large Vocabulary Speech Recognition
Dong Yu,Li Deng,Frank Seide +2 more
TL;DR: Evaluation on Switchboard tasks indicates that DTNNs can outperform the already high-performing DNNs with 4-5% and 3% relative word error reduction, respectively, using 30-hr and 309-hr training sets.
Proceedings ArticleDOI
A study on multilingual acoustic modeling for large vocabulary ASR
TL;DR: The results demonstrate that when the context coverage is poor in language-specific training, one tenth of the adaptation data can be used to achieve equivalent performance in cross-lingual speech recognition.
Posted Content
Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks.
TL;DR: In this article, the authors argue that the improved accuracy achieved by the DNNs is the result of their ability to extract discriminative internal representations that are robust to the many sources of variability in speech signals.