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.

Adam: A Method for Stochastic Optimization

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.

Deep learning

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.

/pdf/generative-adversarial-nets-1ofhan3sbf.pdf

Generative Adversarial Nets

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.

Deep Learning

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 …

I and i

On the localness modeling for the self-attention based end-to-end speech synthesis.

In recent years, there has been a renewed interest in the use of artificial neural networks (ANNs) for speech applications, and it seems that a new trend to move the speech technology forward has begun. Two main contributions have triggered such a new trend: 1) a major advance has been made in training the weights in deep neural networks (DNNs), and a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture has outperformed a conventional Gaussian mixture model hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system on a challenging business search dataset, and 2) it has been shown that phoneme classification can be boosted by using a hierarchical structure of multi-layer perceptrons (MLPs) trained to model long-span temporal patterns with beneficial effects on language recognition tasks. In this work, we combine these two lines of research and demonstrate that word recognition accuracy can be significantly enhanced by arranging DNNs in a hierarchical structure to model long-term energy trajectories. The proposed solution has been evaluated on the 5000-word Wall Street Journal task, resulting in consistent and significant improvements in both phone and word recognition accuracy rates. We have also analyzed the effects of various modeling choices on the system performance, and several architectural solutions have been compared.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Marco2013IEEE.pdf

Speech Recognition Using Long-Span Temporal Patterns in a Deep Network Model

We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. In the PAC-RNN, we can consider that, given the new, current frame information, the main DNN makes a correction on the prediction made by the prediction DNN. Alternatively, it can be viewed as adapting the main DNN's behavior based on the prediction DNN's prediction. Experiments on the TIMIT phone recognition task indicate that the PAC-RNN outperforms DNN, RNN, and LSTM with 2.4%, 2.1%, and 1.9% absolute phone accuracy improvement, respectively. We found that incorporating the prediction objective and including the recurrent loop are both important to boost the performance of the PAC-RNN.

/pdf/speech-recognition-with-prediction-adaptation-correction-a5l80936hq.pdf

Speech recognition with prediction-adaptation-correction recurrent neural networks

Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues associated with the construction of audio-visual speech recognition (AVSR) systems are addressed. First, the basic architecture designs i.e. end-to-end and hybrid of AVSR systems are investigated. Second, purposefully designed modality fusion gates are used to robustly integrate the audio and visual features. Third, in contrast to a traditional pipelined architecture containing explicit speech separation and recognition components, a streamlined and integrated AVSR system optimized consistently using the lattice-free MMI (LF-MMI) discriminative criterion is also proposed. The proposed LF-MMI time-delay neural network (TDNN) system establishes the state-of-the-art for the LRS2 dataset. Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29.98\% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system. Consistent performance improvements of 4.89\% absolute in WER reduction over the baseline AVSR system using feature fusion are also obtained.

Audio-visual Recognition of Overlapped speech for the LRS2 dataset

Although great progress has been made in automatic speech recognition (ASR), significant performance degradation still exists in noisy environments. In this paper, a novel factor-aware training framework, named neural network-based multifactor aware joint training , is proposed to improve the recognition accuracy for noise robust speech recognition. This approach is a structured model which integrates several different functional modules into one computational deep model. We explore and extract speaker, phone, and environment factor representations using deep neural networks (DNNs), which are integrated into the main ASR DNN to improve classification accuracy. In addition, the hidden activations in the main ASR DNN are used to improve factor extraction, which in turn helps the ASR DNN. All the model parameters, including those in the ASR DNN and factor extraction DNNs, are jointly optimized under the multitask learning framework. Unlike prior traditional techniques for the factor-aware training, our approach requires no explicit separate stages for factor extraction and adaptation. Moreover, the proposed neural network-based multifactor aware joint training can be easily combined with the conventional factor-aware training which uses the explicit factors, such as i-vector, noise energy, and $T60$ value to obtain additional improvement. The proposed method is evaluated on two main noise robust tasks: the AMI single distant microphone task in which reverberation is the main concern, and the Aurora4 task in which multiple noise types exist. Experiments on both tasks show that the proposed model can significantly reduce word error rate (WER). The best configuration achieved more than 15% relative reduction in WER over the baselines on these two tasks.

Dong Yu

Papers

On the localness modeling for the self-attention based end-to-end speech synthesis.

Speech Recognition Using Long-Span Temporal Patterns in a Deep Network Model

Speech recognition with prediction-adaptation-correction recurrent neural networks

Audio-visual Recognition of Overlapped speech for the LRS2 dataset

Neural Network Based Multi-Factor Aware Joint Training for Robust Speech Recognition