121 citations · 155 across the 14 of their papers we have counts for
6 papers · 1 filter
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing
Xiaodong Cui, George Saon, Tohru Nagano +4
We introduce two techniques, length perturbation and n-best based label smoothing, to improve generalization of deep neural network (DNN) acoustic models for automatic speech recog…
Reducing Exposure Bias in Training Recurrent Neural Network Transducers
Xiaodong Cui, Brian Kingsbury, George Saon +2
When recurrent neural network transducers (RNNTs) are trained using the typical maximum likelihood criterion, the prediction network is trained only on ground truth label sequences…
Challenging the Boundaries of Speech Recognition: The MALACH Corpus
Michael Picheny, Zóltan Tüske, Brian Kingsbury +3
There has been huge progress in speech recognition over the last several years. Tasks once thought extremely difficult, such as SWITCHBOARD, now approach levels of human performanc…
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
Xiaodong Cui, Michael Picheny
Evolutionary stochastic gradient descent (ESGD) was proposed as a population-based approach that combines the merits of gradient-aware and gradient-free optimization algorithms for…
Embedding-Based Speaker Adaptive Training of Deep Neural Networks
Xiaodong Cui, Vaibhava Goel, George Saon
An embedding-based speaker adaptive training (SAT) approach is proposed and investigated in this paper for deep neural network acoustic modeling. In this approach, speaker embeddin…
English Conversational Telephone Speech Recognition by Humans and Machines
George Saon, Gakuto Kurata, Tom Sercu +9
One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major sp…