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.

Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention.

Abstract: In this paper, we propose a deep convolutional neural network (CNN) with layer-wise context expansion and location-based attention, for large vocabulary speech recognition. In our model each higher layer uses information from broader contexts, along both the time and frequency dimensions, than its immediate lower layer. We show that both the layer-wise context expansion and the location-based attention can be implemented using the element-wise matrix product and the convolution operation. For this reason, contrary to other CNNs, no pooling operation is used in our model. Experiments on the 309hr Switchboard task and the 375hr short message dictation task indicates that our model outperforms both the DNN and LSTM significantly.

Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition

Abstract: Generation of high-precision sub-phonetic attribute (also known as phonological features) and phone lattices is a key frontend component for detection-based bottom-up speech recognition. In this paper we employ deep neural networks (DNNs) to improve detection accuracy over conventional shallow MLPs (multi-layer perceptrons) with one hidden layer. A range of DNN architectures with five to seven hidden layers and up to 2048 hidden units per layer have been explored. Training on the SI84 and testing on the Nov92 WSJ data, the proposed DNNs achieve significant improvements over the shallow MLPs, producing greater than 90% frame-level attribute estimation accuracies for all 21 attributes tested for the full system. On the phone detection task, we also obtain excellent frame-level accuracy of 86.6%. With this level of high-precision detection of basic speech units we have opened the door to a new family of flexible speech recognition system design for both top-down and bottom-up, lattice-based search strategies and knowledge integration.

Neural Spatial Filter: Target Speaker Speech Separation Assisted with Directional Information.

Abstract: The recent exploration of deep learning for supervised speech separation has significantly accelerated the progress on the multi-talker speech separation problem. The multi-channel approaches have attracted much research attention due to the benefit of spatial information. In this paper, integrated with the power spectra and inter-channel spatial features at the input level, we explore to leverage directional features, which imply the speaker source from the desired target direction, for target speaker separation. In addition, we incorporate an attention mechanism to dynamically tune the model’s attention to the reliable input features to alleviate spatial ambiguity problem when multiple speakers are closely located. We demonstrate, on the far-field WSJ0 2-mix dataset, that our proposed approach significantly improves the performance of speech separation against the baseline single-channel and multi-channel speech separation methods.

A comprehensive study of speech separation: spectrogram vs waveform separation

Abstract: Speech separation has been studied widely for single-channel close-talk microphone recordings over the past few years; developed solutions are mostly in frequency-domain. Recently, a raw audio waveform separation network (TasNet) is introduced for single-channel data, with achieving high Si-SNR (scale-invariant source-to-noise ratio) and SDR (source-to-distortion ratio) comparing against the state-of-the-art solution in frequency-domain. In this study, we incorporate effective components of the TasNet into a frequency-domain separation method. We compare both for alternative scenarios. We introduce a solution for directly optimizing the separation criterion in frequency-domain networks. In addition to speech separation objective and subjective measurements, we evaluate the separation performance on a speech recognition task as well. We study the speech separation problem for far-field data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information. For our experiments, we simulated multi-channel spatialized reverberate WSJ0-2mix dataset. Our experimental results show that spectrogram separation can achieve competitive performance with better network design. Multi-channel framework as well is shown to improve the single-channel performance relatively up to +35.5% and +46% in terms of WER and SDR, respectively.

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Abstract: We present our study on semi-supervised Gaussian mixture model (GMM) hidden Markov model (HMM) and deep neural network (DNN) HMM acoustic model training. We analyze the impact of transcription quality and data sampling approach on the performance of the resulting model, and propose a multisystem combination and confidence re-calibration approach to improve the transcription inference and data selection. Compared to using a single system recognition result and confidence score, our proposed approach reduces the phone error rate of the inferred transcription by 23.8% relatively when top 60% of data are selected. Experiments were conducted on the mobile short message dictation (SMD) task. For the GMM-HMM model, we achieved 7.2% relative word error rate reduction (WERR) against a well-trained narrow-band fMPE+bMMI system by adding 2100 hours of untranscribed data, and 28.2% relative WERR over a wide-band MLE model trained from transcribed out-of-domain voice search data after adding 10K hours of untranscribed SMD data. For the CD-DNN-HMM model, 11.7% and 15.0% relative WERRs are achieved after adding 1K hours of untranscribed data using random and importance sampling, respectively. We also found using large amount of untranscribed data for pretraining does not help. Index Terms: semi-supervised acoustic model training, system combination, confidence re-calibration, importance sampling

Adam: A Method for Stochastic Optimization

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.

Deep learning

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.

Generative Adversarial Nets

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.

Deep Learning

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.

I and i

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 …