A Winnow-Based Approach to Context-Sensitive Spelling Correction

A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their target concepts refer to only a small subset of the features in the space. Under such conditions, multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good theoretical properties. We present an algorithm combining variants of Winnow and weighted-majority voting, and apply it to a problem in the aforementioned class: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting "to" for "too", "casual" for "causal", etc. We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a statistics-based method representing the state of the art for this task. We find: (1) When run with a full (unpruned) set of features, WinSpell achieves accuracies significantly higher than BaySpell was able to achieve in either the pruned or unpruned condition; (2) When compared with other systems in the literature, WinSpell exhibits the highest performance; (3) The primary reason that WinSpell outperforms BaySpell is that WinSpell learns a better linear separator; (4) When run on a test set drawn from a different corpus than the training set was drawn from, WinSpell is better able than BaySpell to adapt, using a strategy we will present that combines supervised learning on the training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language Learning, 1999. 25 page

New methods for deep dictionary learning and for image completion

Digital imaging plays an essential role in many aspects of our daily life. However due to the hardware limitations of the imaging devices, the image measurements are usually inpaired and require further processing to enhance the quality of the raw images in order to enable applications on the user side. Image enhancement aims to improve the information content within image measurements by exploiting the properties of the target image and the forward model of the imaging device. In this thesis, we aim to tackle two specific image enhancement problems, that is, single image super-resolution and image completion. First, we present a new Deep Analysis Dictionary Model (DeepAM) which consists of multiple layers of analysis dictionaries with associated soft-thresholding operators and a single layer of synthesis dictionary for single image super-resolution. To achieve an effective deep model, each analysis dictionary has been designed to be composed of an Information Preserving Analysis Dictionary (IPAD) which passes essential information from the input signal to output and a Clustering Analysis Dictionary (CAD) which generates discriminative feature representation. The parameters of the deep analysis dictionary model are optimized using a layer-wise learning strategy. We demonstrate that both the proposed deep dictionary design and the learning algorithm are effective. Simulation results show that the proposed method achieves comparable performance with Deep Neural Networks and other existing methods. We then generalize DeepAM to a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries. The convolutional dictionary is more suitable for processing high-dimensional signals like images and has only a small number of free parameters. By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning algorithm. The IPAD and the CAD parts are learned using variations of the proposed convolutional analysis dictionary learning algorithm. We demonstrate that DeepCAM is an effective multi-layer convolutional model and achieves better performance than DeepAM while using a smaller number of parameters. Finally, we present an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from Internet which has been taken at a similar position. The dense correspondence which is estimated using a hierarchical PatchMatch algorithm is usually noisy and with a large occlusion area corresponding to the region to be completed. By modelling the dense correspondence as a smooth field, an Expectation-Maximization (EM) based method is presented to interpolate a smooth field over the occlusion area which is then used to transfer image content from the exemplar image to the input image. Color correction is further applied to diminish the possible color differences between the input image and the exemplar image. Numerical results demonstrate that the proposed image completion algorithm is able to achieve photo realistic image completion results.Open Acces

Sign language video retrieval with free-form textual queries

Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with textual queries: given a written query (e.g. a sentence) and a large collection of sign language videos, the objective is to find the signing video that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labelled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task.This work was supported by the project PID2020-117142GB-I00, funded by MCIN/ AEI /10.13039/501100011033, ANR project CorVis ANR-21-CE23-0003- 01, and gifts from Google and Adobe. AD received support from la Caixa Foundation (ID 100010434), fellowship code LCF/BQ/IN18/11660029.Peer ReviewedObjectius de Desenvolupament Sostenible::10 - Reducció de les DesigualtatsObjectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtats::10.2 - Per a 2030, potenciar i promoure la inclusió social, econòmica i política de totes les persones, independentment de l’edat, sexe, discapacitat, raça, ètnia, origen, religió, situació econòmica o altra condicióPostprint (author's final draft

Enhancement automatic speech recognition by deep neural networks

The performance of speech recognition tasks utilizing systems based on deep learning has improved dramatically in recent years by utilizing different deep designs and learning methodologies. A popular way to boosting the number of training data is called Data Augmentation (DA), and research shows that using DA is effective in teaching neural network models how to make invariant predictions. furthermore, EM approaches have piqued machine-learning researchers' attention as a means of improving classifier performance. In this study, have been presenteded a unique deep neural network speech recognition that employs both EM and DA approaches to improve the system's prediction accuracy. firstly, reveal an approach based on vocal tract length disturbance that already exists and then propose a Feature perturbation is an alternative Data Augmentation approach. in order to make amendment training data sets. This is followed by an integration of the posterior probabilities obtained from several DNN acoustic models trained on diverse datasets. The study's findings reveal that the proposed system's recognition skills have improved

Sparse and Low-rank Modeling for Automatic Speech Recognition

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that whenever a speech signal is measured in a high-dimensional feature space, the true class information is embedded in low-dimensional subspaces whereas noise is scattered as random high-dimensional erroneous estimations in the features. In this context, the contribution of this thesis is twofold: (i) identify sparse and low-rank modeling approaches as excellent tools for extracting the class-specific low-dimensional subspaces in speech features, and (ii) employ these tools under novel ASR frameworks to enrich the acoustic information present in the speech features towards the goal of improving ASR. Techniques developed in this thesis focus on deep neural network (DNN) based posterior features which, under the sparse and low-rank modeling approaches, unveil the underlying class-specific low-dimensional subspaces very elegantly. In this thesis, we tackle ASR tasks of varying difficulty, ranging from isolated word recognition (IWR) and connected digit recognition (CDR) to large-vocabulary continuous speech recognition (LVCSR). For IWR and CDR, we propose a novel \textit{Compressive Sensing} (CS) perspective towards ASR. Here exemplar-based speech recognition is posed as a problem of recovering sparse high-dimensional word representations from compressed low-dimensional phonetic representations. In the context of LVCSR, this thesis argues that albeit their power in representation learning, DNN based acoustic models still have room for improvement in exploiting the \textit{union of low-dimensional subspaces} structure of speech data. Therefore, this thesis proposes to enhance DNN posteriors by projecting them onto the manifolds of the underlying classes using principal component analysis (PCA) or compressive sensing based dictionaries. Projected posteriors are shown to be more accurate training targets for learning better acoustic models, resulting in improved ASR performance. The proposed approach is evaluated on both close-talk and far-field conditions, confirming the importance of sparse and low-rank modeling of speech in building a robust ASR framework. Finally, the conclusions of this thesis are further consolidated by an information theoretic analysis approach which explicitly quantifies the contribution of proposed techniques in improving ASR

Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DNN) to estimate class-conditional posterior probabilities. The posterior probabilities are used for acoustic modeling in hidden Markov models (HMM), and form a hybrid DNN-HMM which is now the leading edge approach to solve ASR problems. The present work builds upon the hypothesis that the optimal acoustic models are sparse and lie on multiple low-rank probability subspaces. Hence, the main goal of this Master project aimed at investigating different ways to restructure the DNN outputs using low-rank representation. Exploiting a large number of training posterior vectors, the underlying low-dimensional subspace can be identified, and low-rank decomposition enables separation of the “optimal” posteriors from the spurious (unstructured) uncertainties at the DNN output. Experiments demonstrate that low-rank representation can enhance posterior probability estimation, and lead to higher ASR accuracy. The posteriors are grouped according to their subspace similarities, and structured through low-rank decomposition. Furthermore, a novel hashing technique is proposed exploiting the low-rank property of posterior subspaces that enables fast search in the space of posterior exemplars