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Author

Dong Yu

Bio: 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
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Patent
Alejandro Acero1, Dong Yu1, JJ Odell1, Milind Mahajan1, Peter K. L. Mau1 
10 Feb 2005
TL;DR: The authors utilize a filter to remove a variety of non-dictated words from data based on probability and improve the effectiveness of creating a language model, which is similar to our approach.
Abstract: The method and apparatus utilize a filter to remove a variety of non-dictated words from data based on probability and improve the effectiveness of creating a language model.

28 citations

Proceedings ArticleDOI
Dong Yu1, Li Deng1, Frank Seide1
01 Sep 2012
TL;DR: Evaluation on 30hr Switchboard task indicates that DTNNs can outperform DNNs with similar number of parameters with 5% relative word error reduction, and is extended to deep tensor neural networks (DTNNs) in which one or more layers are double-projection and tensor layers.
Abstract: Recently, we proposed and developed the context-dependent deep neural network hidden Markov models (CD-DNN-HMMs) for large vocabulary speech recognition and achieved highly promising recognition results including over one third fewer word errors than the discriminatively trained, conventional HMM-based systems on the 300hr Switchboard benchmark task. In this paper, we extend DNNs to deep tensor neural networks (DTNNs) in which one or more layers are double-projection and tensor layers. The basic idea of the DTNN comes from our realization that many factors interact with each other to predict the output. To represent these interactions, we project the input to two nonlinear subspaces through the double-projection layer and model the interactions between these two subspaces and the output neurons through a tensor with three-way connections. Evaluation on 30hr Switchboard task indicates that DTNNs can outperform DNNs with similar number of parameters with 5% relative word error reduction

28 citations

Patent
Ye-Yi Wang1, Yun-Cheng Ju1, Dong Yu1
03 Aug 2007
TL;DR: In this paper, a confidence measure generator was proposed to calculate an overall confidence measure for voice search results based upon the features received from the speech recognizer, search component, and dialog manager.
Abstract: A voice search system has a speech recognizer, a search component, and a dialog manager. A confidence measure generator receives speech recognition features from the speech recognizer, search features from the search component, and dialog features from the dialog manager, and calculates an overall confidence measure for voice search results based upon the features received. The invention can be extended to include the generation of additional features, based on those received from the individual components of the voice search system.

28 citations

Proceedings ArticleDOI
04 May 2020
TL;DR: It is shown that by using the same synthetic training data, the same neural networks gain significant performance improvement on real test sets in far-field speech recognition by 1.58% and keyword spotting by 21%, without fine-tuning using real impulse responses.
Abstract: We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method can only model specular reflections in simple room settings. We show that by using our synthetic training data, the same neural networks gain significant performance improvement on real test sets in far-field speech recognition by 1.58% and keyword spotting by 21%, without fine-tuning using real impulse responses.

27 citations

Proceedings ArticleDOI
Chengzhu Yu1, Chunlei Zhang2, Chao Weng1, Jia Cui1, Dong Yu1 
02 Sep 2018
TL;DR: This study empirically investigate advanced model initializations and training strategies to achieve competitive speech recognition performance on 300 hour subset of the Switchboard task (SWB-300Hr) and investigates the use of hierarchical CTC pretraining for improved model initialization.
Abstract: Acoustic-to-word (A2W) prediction model based on Connectionist Temporal Classification (CTC) criterion has gained increasing interest in recent studies. Although previous studies have shown that A2W system could achieve competitive Word Error Rate (WER), there is still performance gap compared with the conventional speech recognition system when the amount of training data is not exceptionally large. In this study, we empirically investigate advanced model initializations and training strategies to achieve competitive speech recognition performance on 300 hour subset of the Switchboard task (SWB-300Hr). We first investigate the use of hierarchical CTC pretraining for improved model initialization. We also explore curriculum training strategy to gradually increase the target vocabulary size from 10k to 20k. Finally, joint CTC and Cross Entropy (CE) training techniques are studied to further improve the performance of A2W system. The combination of hierarchical-CTC model initialization, curriculum training and joint CTC-CE training translates to a relative of 12.1% reduction in WER. Our final A2W system evaluated on Hub5-2000 test sets achieves a WER of 11.4/20.8 for Switchboard and CallHome parts without using language model and complex decoder.

27 citations


Cited by
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Proceedings Article
01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

111,197 citations

Journal ArticleDOI
28 May 2015-Nature
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

46,982 citations

Journal ArticleDOI
08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

38,211 citations

Book
18 Nov 2016
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

38,208 citations

Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations