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.

Using collective information in semi-supervised learning for speech recognition

Abstract: Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learning algorithm for automatic speech recognition. The algorithm determines whether a hypothesized transcription should be used in the training by taking into consideration collective information from all utterances available instead of solely based on the confidence from that utterance itself. It estimates the expected entropy reduction each utterance and transcription pair may cause to the whole unlabeled dataset and choose the ones with the positive gains. We compare our algorithm with existing confidence-based semi-supervised learning algorithm and show that the former can consistently outperform the latter when the same amount of utterances is selected into the training set. We also indicate that our algorithm may determine the cutoff-point in a principled way by demonstrating that the point it finds is very close to the achievable peak point.

A Generative Modeling Framework for Structured Hidden Speech Dynamics

Abstract: We outline a structured speech model, as a special and perhaps extreme form of probabilistic generative modeling. The model is equipped with long-contextual-span capabilities that are missing in the HMM approach. Compact (and physically meaningful) parameterization of the model is made possible by the continuity constraint in the hidden vocal tract resonance (VTR) domain. The target-directed VTR dynamics jointly characterize coarticulation and incomplete articulation (reduction). Preliminary evaluation results are presented on the standard TIMIT phonetic recognition task, showing the best result in this task reported in the literature without using many heterogeneous classifier combinations. The pros and cons of our structured generative modeling approach, in comparison with the structured discriminative classification approach, are discussed.

Learning statistically characterized resonance targets in a hidden trajectory model of speech coarticulation and reduction.

Abstract: We report our new development of a hidden trajectory model for co-articulated, time-varying patterns of speech. The model uses bi-directional filtering of vocal tract resonance targets to jointly represent contextual variation and phonetic reduction in speech acoustics. A novel maximum-likelihood-based learning algorithm is presented that accurately estimates the distributional parameters of the resonance targets. The results of the estimates are analyzed and shown to be consistent with all the relevant acoustic-phonetic facts and intuitions. Phonetic recognition experiments demonstrate that the model with more rigorous target training outperforms the most recent earlier version of the model, producing 17.5% fewer errors in N-best rescoring.

Robust speech recognition using cepstral minimum-mean-square-error noise suppressor

Abstract: We present an efficient and effective nonlinear feature-domain noise suppression algorithm, motivated by the minimum mean-square-error (MMSE) optimization criterion, for noiserobust speech recognition. Distinguishing from the log-MMSE spectral amplitude noise suppressor proposed by Ephraim and Malah (E&M), our new algorithm is aimed to minimize the error expressed explicitly for the Mel-frequency cepstra instead of discrete Fourier transform (DFT) spectra, and it operates on the Mel-frequency filter bank’s output. As a consequence, the statistics used to estimate the suppression factor become vastly different from those used in the E&M log-MMSE suppressor. Our algorithm is significantly more efficient than the E&M’s log-MMSE suppressor since the number of the channels in the Mel-frequency filter bank is much smaller (23 in our case) than the number of bins (256) in DFT.We have conducted extensive speech recognition experiments on the standard Aurora-3 task. The experimental results demonstrate a reduction of the recognition word error rate by 48% over the standard ICSLP02 baseline, 26% over the cepstral mean normalization baseline, and 13% over the popular E&M’s log-MMSE noise suppressor. The experiments also show that our new algorithm performs slightly better than the ETSI advanced front end (AFE) on the well-matched and mid-mismatched settings, and has 8% and 10% fewer errors than our earlier SPLICE (stereo-based piecewise linear compensation for environments) system on these settings, respectively.

A Framework for Adapting DNN Speaker Embedding Across Languages

Abstract: Language mismatch remains a major hindrance to the extensive deployment of speaker verification (SV) systems. Current language adaptation methods in SV mainly rely on linear projection in embedding space; i.e., adaptation is carried out after the speaker embeddings have been created, which underutilizes the powerful representation of deep neural networks. This article proposes a maximum mean discrepancy (MMD) based framework for adapting deep neural network (DNN) speaker embedding across languages, featuring m ulti-level domain loss, s eparate batch normalization, and c onsistency regularization. We refer to the framework as MSC. We show that (1) minimizing domain discrepancy at both frame- and utterance-levels performs significantly better than at utterance-level alone; (2) separating the source-domain data from the target-domain in batch normalization improves adaptation performance; and (3) data augmentation can be utilized in the unlabelled target-domain through consistency regularization. By combining these findings, we achieve an EER of 8.69% and 7.95% in NIST SRE 2016 and 2018, respectively, which are significantly better than the previously proposed DNN adaptation methods. Our framework also works well with backend adaptation. By combining the proposed framework with backend adaptation, we achieve an 11.8% improvement over the backend adaptation in SRE18. When applying our framework to a 121-layer Densenet, we achieved an EER of 7.81% and 7.02% in NIST SRE 2016 and 2018, respectively.

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 …