Improving Deep Representation Learning with Complex and Multimodal Data.

Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech recognition, and text understanding. However, learning representation from complex high-dimensional sensory data is challenging since there exist many irrelevant factors of variation (e.g., data transformation, random noise). On the other hand, to build an end-to-end prediction system for structured output variables, one needs to incorporate probabilistic inference to properly model a mapping from single input to possible configurations of output variables. This thesis addresses limitations of current representation learning in two parts. The first part discusses efficient learning algorithms of invariant representation based on restricted Boltzmann machines (RBMs). Pointing out the difficulty of learning, we develop an efficient initialization method for sparse and convolutional RBMs. On top of that, we develop variants of RBM that learn representations invariant to data transformations such as translation, rotation, or scale variation by pooling the filter responses of input data after a transformation, or to irrelevant patterns such as random or structured noise, by jointly performing feature selection and feature learning. We demonstrate improved performance on visual object recognition and weakly supervised foreground object segmentation. The second part discusses conditional graphical models and learning frameworks for structured output variables using deep generative models as prior. For example, we combine the best properties of the CRF and the RBM to enforce both local and global (e.g., object shape) consistencies for visual object segmentation. Furthermore, we develop a deep conditional generative model of structured output variables, which is an end-to-end system trainable by backpropagation. We demonstrate the importance of global prior and probabilistic inference for visual object segmentation. Second, we develop a novel multimodal learning framework by casting the problem into structured output representation learning problems, where the output is one data modality to be predicted from the other modalities, and vice versa. We explain as to how our method could be more effective than maximum likelihood learning and demonstrate the state-of-the-art performance on visual-text and visual-only recognition tasks.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113549/1/kihyuks_1.pd

Impact of Noise in Automatic Speech Recognition for Low-Resourced Languages

The usage of deep learning algorithms has resulted in significant progress in auto- matic speech recognition (ASR). The ASR models may require over a thousand hours of speech data to accurately recognize the speech. There have been case studies that have indicated that there are certain factors like noise, acoustic distorting conditions, and voice quality that has affected the performance of speech recognition. In this research, we investigate the impact of noise on Automatic Speech Recognition and explore novel methods for developing noise-robust ASR models using the Tamil lan- guage dataset with limited resources. We are using the speech dataset provided by SpeechOcean.com and Microsoft for the Indian languages. We add several kinds of noise to the dataset and find out how these noises impact the ASR performance. We also determine whether certain data augmentation methods like raw data augmen- tation and spectrogram augmentation (SpecAugment) are better suited to different types of noises. Our results show that all noises, regardless of the type, had an impact on ASR performance, and upgrading the architecture alone were unable to mitigate the impact of noise. Raw data augmentation enhances ASR performance on both clean data and noise-mixed data, however, this was not the case with SpecAugment on the same test sets. As a result, raw data augmentation performs way better than SpecAugment over the baseline models

Noise-Robust Speech Recognition Using Deep Neural Network

Ph.DDOCTOR OF PHILOSOPH

Single channel audio separation using deep neural networks and matrix factorizations

PhD ThesisSource Separation has become a significant research topic in the signal processing community and the machine learning area. Due to numerous applications, such as automatic speech recognition and speech communication, separation of target speech from the mixed signal is of great importance. In many practical applications, speech separation from a single recorder is most desirable from an application standpoint. In this thesis, two novel approaches have been proposed to address this single channel audio separation problem. This thesis first reviews traditional approaches for single channel source separation, and later elicits a generic approach, which is more capable of feature learning, i.e. deep graphical models. In the first part of this thesis, a novel approach based on matrix factorization and hierarchical model has been proposed. In this work, an artificial stereo mixture is formulated to provide extra information. In addition, a hybrid framework that combines the generalized Expectation-Maximization algorithm with a multiplicative update rule is proposed to optimize the parameters of a matrix factorization based approach to approximatively separate the mixture. Furthermore, a hierarchical model based on an extreme learning machine is developed to check the validity of the approximately separated sources followed by an energy minimization method to further improve the quality of the separated sources by generating a time-frequency mask. Various experiments have been conducted and the obtained results have shown that the proposed approach outperforms conventional approaches not only in reduction of computational complexity, but also the separation performance. In the second part, a deep neural network based ensemble system is proposed. In this work, the complementary property of different features are fully explored by ‘wide’ and ‘forward’ ensemble system. In addition, instead of using the features learned from the output layer, the features learned from the penultimate layer are investigated. The final embedded features are classified with an extreme learning machine to generate a binary mask to separate a mixed signal. The experiment focuses on speech in the presence of music and the obtained results demonstrated that the proposed ensemble system has the ability to explore the complementary property of various features thoroughly under various conditions with promising separation performance

Beyond PCA: Deep Learning Approaches for Face Modeling and Aging

Modeling faces with large variations has been a challenging task in computer vision. These variations such as expressions, poses and occlusions are usually complex and non-linear. Moreover, new facial images also come with their own characteristic artifacts greatly diverse. Therefore, a good face modeling approach needs to be carefully designed for flexibly adapting to these challenging issues. Recently, Deep Learning approach has gained significant attention as one of the emerging research topics in both higher-level representation of data and the distribution of observations. Thanks to the nonlinear structure of deep learning models and the strength of latent variables organized in hidden layers, it can efficiently capture variations and structures in complex data. Inspired by this motivation, we present two novel approaches, i.e. Deep Appearance Models (DAM) and Robust Deep Appearance Models (RDAM), to accurately capture both shape and texture of face images under large variations. In DAM, three crucial components represented in hierarchical layers are modeled using Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAM has shown its potential in inferencing a representation for new face images under various challenging conditions. An improved version of DAM, named Robust DAM (RDAM), is also introduced to better handle the occluded face areas and, therefore, produces more plausible reconstruction results. These proposed approaches are evaluated in various applications to demonstrate their robustness and capabilities, e.g. facial super-resolution reconstruction, facial off-angle reconstruction, facial occlusion removal and age estimation using challenging face databases: Labeled Face Parts in the Wild (LFPW), Helen and FG-NET. Comparing to classical and other deep learning based approaches, the proposed DAM and RDAM achieve competitive results in those applications, thus this showed their advantages in handling occlusions, facial representation, and reconstruction. In addition to DAM and RDAM that are mainly used for modeling single facial image, the second part of the thesis focuses on novel deep models, i.e. Temporal Restricted Boltzmann Machines (TRBM) and tractable Temporal Non-volume Preserving (TNVP) approaches, to further model face sequences. By exploiting the additional temporal relationships presented in sequence data, the proposed models have their advantages in predicting the future of a sequence from its past. In the application of face age progression, age regression, and age-invariant face recognition, these models have shown their potential not only in efficiently capturing the non-linear age related variance but also producing a smooth synthesis in age progression across faces. Moreover, the structure of TNVP can be transformed into a deep convolutional network while keeping the advantages of probabilistic models with tractable log-likelihood density estimation. The proposed approach is evaluated in terms of synthesizing age-progressed faces and cross-age face verification. It consistently shows the state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH, our collected large-scale aging database named AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A large-scale face verification on Megaface challenge 1 is also performed to further show the advantages of our proposed approach

Human Action Recognition Using Deep Probabilistic Graphical Models

Building intelligent systems that are capable of representing or extracting high-level representations from high-dimensional sensory data lies at the core of solving many A.I. related tasks. Human action recognition is an important topic in computer vision that lies in high-dimensional space. Its applications include robotics, video surveillance, human-computer interaction, user interface design, and multi-media video retrieval amongst others. A number of approaches have been proposed to extract representative features from high-dimensional temporal data, most commonly hard wired geometric or bio-inspired shape context features. This thesis first demonstrates some \emph{ad-hoc} hand-crafted rules for effectively encoding motion features, and later elicits a more generic approach for incorporating structured feature learning and reasoning, \ie deep probabilistic graphical models. The hierarchial dynamic framework first extracts high level features and then uses the learned representation for estimating emission probability to infer action sequences. We show that better action recognition can be achieved by replacing gaussian mixture models by Deep Neural Networks that contain many layers of features to predict probability distributions over states of Markov Models. The framework can be easily extended to include an ergodic state to segment and recognise actions simultaneously. The first part of the thesis focuses on analysis and applications of hand-crafted features for human action representation and classification. We show that the ``hard coded" concept of correlogram can incorporate correlations between time domain sequences and we further investigate multi-modal inputs, \eg depth sensor input and its unique traits for action recognition. The second part of this thesis focuses on marrying probabilistic graphical models with Deep Neural Networks (both Deep Belief Networks and Deep 3D Convolutional Neural Networks) for structured sequence prediction. The proposed Deep Dynamic Neural Network exhibits its general framework for structured 2D data representation and classification. This inspires us to further investigate for applying various graphical models for time-variant video sequences

Masked Conditional Neural Networks for Sound Recognition

Sound recognition has been studied for decades to grant machines the human hearing ability. The advances in this field help in a range of applications, from industrial ones such as fault detection in machines and noise monitoring to household applications such as surveillance and hearing aids. The problem of sound recognition like any pattern recognition task involves the reliability of the extracted features and the recognition model. The problem has been approached through decades of crafted features used collaboratively with models based on neural networks or statistical models such as Gaussian Mixtures and Hidden Markov models. Neural networks are currently being considered as a method to automate the feature extraction stage together with the already incorporated role of recognition. The performance of such models is approaching handcrafted features. Current neural network based models are not primarily designed for the nature of the sound signal, which may not optimally harness distinctive properties of the signal. This thesis proposes neural network models that exploit the nature of the time-frequency representation of the sound signal. We propose the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN). The CLNN is designed to account for the temporal dimension of a signal and behaves as the framework for the MCLNN. The MCLNN allows a filterbank-like behaviour to be embedded within the network using a specially designed binary mask. The masking subdivides the frequency range of a signal into bands and allows concurrent consideration of different feature combinations analogous to the manual handcrafting of the optimum set of features for a recognition task. The proposed models have been evaluated through an extensive set of experiments using a range of publicly available datasets of music genres and environmental sounds, where they surpass state-of-the-art Convolutional Neural Networks and several hand-crafted attempts