Efficient Wait-k Models for Simultaneous Machine Translation

Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for this problem. They first read k source tokens, after which they alternate between producing a target token and reading another source token. We investigate the behavior of wait-k decoding in low resource settings for spoken corpora using IWSLT datasets. We improve training of these models using unidirectional encoders, and training across multiple values of k. Experiments with Transformer and 2D-convolutional architectures show that our wait-k models generalize well across a wide range of latency levels. We also show that the 2D-convolution architecture is competitive with Transformers for simultaneous translation of spoken language.Comment: Accepted at INTERSPEECH 202

Speech recognition using linear dynamic models.

The majority of automatic speech recognition (ASR) systems rely on hidden Markov models, in which Gaussian mixtures model the output distributions associated with sub-phone states. This approach, whilst successful, models consecutive feature vectors (augmented to include derivative information) as statistically independent. Furthermore, spatial correlations present in speech parameters are frequently ignored through the use of diagonal covariance matrices. This paper continues the work of Digalakis and others who proposed instead a first-order linear state-space model which has the capacity to model underlying dynamics, and furthermore give a model of spatial correlations. This paper examines the assumptions made in applying such a model and shows that the addition of a hidden dynamic state leads to increases in accuracy over otherwise equivalent static models. We also propose a time-asynchronous decoding strategy suited to recognition with segment models. We describe implementation of decoding for linear dynamic models and present TIMIT phone recognition results

Advances in deep learning methods for speech recognition and understanding

Ce travail expose plusieurs études dans les domaines de la reconnaissance de la parole et compréhension du langage parlé. La compréhension sémantique du langage parlé est un sous-domaine important de l'intelligence artificielle. Le traitement de la parole intéresse depuis longtemps les chercheurs, puisque la parole est une des charactéristiques qui definit l'être humain. Avec le développement du réseau neuronal artificiel, le domaine a connu une évolution rapide à la fois en terme de précision et de perception humaine. Une autre étape importante a été franchie avec le développement d'approches bout en bout. De telles approches permettent une coadaptation de toutes les parties du modèle, ce qui augmente ainsi les performances, et ce qui simplifie la procédure d'entrainement. Les modèles de bout en bout sont devenus réalisables avec la quantité croissante de données disponibles, de ressources informatiques et, surtout, avec de nombreux développements architecturaux innovateurs. Néanmoins, les approches traditionnelles (qui ne sont pas bout en bout) sont toujours pertinentes pour le traitement de la parole en raison des données difficiles dans les environnements bruyants, de la parole avec un accent et de la grande variété de dialectes. Dans le premier travail, nous explorons la reconnaissance de la parole hybride dans des environnements bruyants. Nous proposons de traiter la reconnaissance de la parole, qui fonctionne dans un nouvel environnement composé de différents bruits inconnus, comme une tâche d'adaptation de domaine. Pour cela, nous utilisons la nouvelle technique à l'époque de l'adaptation du domaine antagoniste. En résumé, ces travaux antérieurs proposaient de former des caractéristiques de manière à ce qu'elles soient distinctives pour la tâche principale, mais non-distinctive pour la tâche secondaire. Cette tâche secondaire est conçue pour être la tâche de reconnaissance de domaine. Ainsi, les fonctionnalités entraînées sont invariantes vis-à-vis du domaine considéré. Dans notre travail, nous adoptons cette technique et la modifions pour la tâche de reconnaissance de la parole dans un environnement bruyant. Dans le second travail, nous développons une méthode générale pour la régularisation des réseaux génératif récurrents. Il est connu que les réseaux récurrents ont souvent des difficultés à rester sur le même chemin, lors de la production de sorties longues. Bien qu'il soit possible d'utiliser des réseaux bidirectionnels pour une meilleure traitement de séquences pour l'apprentissage des charactéristiques, qui n'est pas applicable au cas génératif. Nous avons développé un moyen d'améliorer la cohérence de la production de longues séquences avec des réseaux récurrents. Nous proposons un moyen de construire un modèle similaire à un réseau bidirectionnel. L'idée centrale est d'utiliser une perte L2 entre les réseaux récurrents génératifs vers l'avant et vers l'arrière. Nous fournissons une évaluation expérimentale sur une multitude de tâches et d'ensembles de données, y compris la reconnaissance vocale, le sous-titrage d'images et la modélisation du langage. Dans le troisième article, nous étudions la possibilité de développer un identificateur d'intention de bout en bout pour la compréhension du langage parlé. La compréhension sémantique du langage parlé est une étape importante vers le développement d'une intelligence artificielle de type humain. Nous avons vu que les approches de bout en bout montrent des performances élevées sur les tâches, y compris la traduction automatique et la reconnaissance de la parole. Nous nous inspirons des travaux antérieurs pour développer un système de bout en bout pour la reconnaissance de l'intention.This work presents several studies in the areas of speech recognition and understanding. The semantic speech understanding is an important sub-domain of the broader field of artificial intelligence. Speech processing has had interest from the researchers for long time because language is one of the defining characteristics of a human being. With the development of neural networks, the domain has seen rapid progress both in terms of accuracy and human perception. Another important milestone was achieved with the development of end-to-end approaches. Such approaches allow co-adaptation of all the parts of the model thus increasing the performance, as well as simplifying the training procedure. End-to-end models became feasible with the increasing amount of available data, computational resources, and most importantly with many novel architectural developments. Nevertheless, traditional, non end-to-end, approaches are still relevant for speech processing due to challenging data in noisy environments, accented speech, and high variety of dialects. In the first work, we explore the hybrid speech recognition in noisy environments. We propose to treat the recognition in the unseen noise condition as the domain adaptation task. For this, we use the novel at the time technique of the adversarial domain adaptation. In the nutshell, this prior work proposed to train features in such a way that they are discriminative for the primary task, but non-discriminative for the secondary task. This secondary task is constructed to be the domain recognition task. Thus, the features trained are invariant towards the domain at hand. In our work, we adopt this technique and modify it for the task of noisy speech recognition. In the second work, we develop a general method for regularizing the generative recurrent networks. It is known that the recurrent networks frequently have difficulties staying on same track when generating long outputs. While it is possible to use bi-directional networks for better sequence aggregation for feature learning, it is not applicable for the generative case. We developed a way improve the consistency of generating long sequences with recurrent networks. We propose a way to construct a model similar to bi-directional network. The key insight is to use a soft L2 loss between the forward and the backward generative recurrent networks. We provide experimental evaluation on a multitude of tasks and datasets, including speech recognition, image captioning, and language modeling. In the third paper, we investigate the possibility of developing an end-to-end intent recognizer for spoken language understanding. The semantic spoken language understanding is an important step towards developing a human-like artificial intelligence. We have seen that the end-to-end approaches show high performance on the tasks including machine translation and speech recognition. We draw the inspiration from the prior works to develop an end-to-end system for intent recognition

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

Design and training of deep reinforcement learning agents

Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effectiveness of goal-directed exploration. Elements of these works cover some of the main building blocks of agents: update methods, neural architectures, exploration strategies, replays, and hierarchy. The second part, devoted to the design of environments, also presents two works. The first one shows how various tasks and demonstrations can be combined to learn complex skill spaces that can then be reused to solve even more challenging tasks. The proposed method, called CoMic, extends previous work on motor primitives by using a single multi-clip motion capture tracking task in conjunction with complementary tasks targeting out-of-distribution movements. The second work addresses a particular type of control method vastly neglected in traditional environments but essential for animals: muscle control. An open source codebase called OstrichRL is proposed, containing a musculoskeletal model of an ostrich, an ensemble of tasks, and motion capture data. The results obtained by training a state-of-the-art agent on the proposed tasks show that controlling such a complex system is very difficult and illustrate the importance of using motion capture data. Elements of these works demonstrate the meticulous work that must go into designing environment parts such as: models, observations, rewards, terminations, resets, steps, and demonstrations. The third part, on the design of infrastructures, presents three works. The first one explains the difference between the types of time limits commonly used in reinforcement learning and why they are often treated inappropriately. In one case, tasks are time-limited by nature and a notion of time should be available to agents to maintain the Markov property of the underlying decision process. In the other case, tasks are not time-limited by nature, but time limits are used for convenience to diversify experiences. This is the most common case. It requires a distinction between time limits and environmental terminations, and bootstrapping should be performed at the end of partial episodes. The second work proposes to unify the most popular deep learning frameworks using a single library called Ivy, and provides new differentiable and framework-agnostic libraries built with it. Four such code bases are provided for gradient-based robot motion planning, mechanics, 3D vision, and differentiable continuous control environments. Finally, the third paper proposes a novel deep reinforcement learning library, called Tonic, built with simplicity and modularity in mind, to accelerate prototyping and evaluation. In particular, it contains implementations of several continuous control agents and a large-scale benchmark. Elements of these works illustrate the different components to consider when building the infrastructure for an experiment: deep learning framework, schedules, and distributed training. Added to these are the various ways to perform evaluations and analyze results for meaningful, interpretable, and reproducible deep reinforcement learning research.Open Acces

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping

Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all\u27 solutions are thus exceedingly difficult to engineer, and highly specialized systems, which represent the state-of-the-art for most types of deployments, are not robust to operating conditions in which their assumptions are not met. This thesis seeks to investigate an alternative approach to these robustness and universality problems by incorporating existing SLAM solutions within a larger framework supported by planning and learning. The central idea is to combine learned models that estimate SLAM algorithm performance under a variety of sensory conditions, in this case neural networks, with planners designed for planning under uncertainty and partial observability, in this case partially observable Markov decision problems (POMDPs). Models of existing SLAM algorithms can be learned, and these models can then be used online to estimate the performance of a range of solutions to the SLAM problem at hand. The POMDP policy then selects the appropriate algorithm, given the estimated performance, cost of switching methods, and other information. This general approach may also be applicable to many other robotics problems that rely on data-fusion, such as grasp planning, motion planning, or object identification

Learning and time : on using memory and curricula for language understanding

Cette thèse présente quelques-unes des étapes entreprises pour pouvoir un jour résoudre le problème de la compréhension du langage naturel et d’apprentissage de dépendances à long terme, dans le but de développer de meilleurs algorithmes d’intelligence artificielle. Cette thèse est écrite comme une thèse par articles, et contient cinq publications scientifiques. Chacun de ces articles propose un nouveau modèle ou algorithme et démontre leur efficacité sur des problèmes qui impliquent des dépendances à long terme ou la compréhension du langage naturel. Malgré le fait que quelque uns de ces modèles n’ont été testés que sur une seule tâche (comme la traduction automatique neuronale), les méthodes proposées sont généralement applicables dans d’autres domaines et sur d’autres tâches. Dans l’introduction de la thèse, nous expliquons quelques concepts fondamentaux de l'entraînement de réseaux de neurones appliqués sur des données séquentielles. Tout d'abord, nous présentons succinctement les réseaux de neurones, puis, de façon plus détaillé, certains algorithmes et méthodes utilisés à travers cette thèse. Dans notre premier article, nous proposons une nouvelle méthode permettant d'utiliser la grande quantité de données monolingue disponible afin d'entraîner des modèles de traduction. Nous avons accompli cela en entraînant d’abord un modèle Long short-term memory (LSTM) sur un large corpus monolingue. Nous lions ensuite la sortie de la couche cachée du modèle avec celle d’un décodeur d’un modèle de traduction automatique. Ce dernier utilise un mécanisme d’attention et est entièrement entraîné par descente de gradient. Nous avons montré que la méthode proposée peut augmenter la performance des modèles de traduction automatique neuronale de façon significative sur les tâches où peu de données multilingues sont disponibles. Notre approche augmente également l’efficacité de l’utilisation des données dans les systèmes de traduction automatique. Nous montrons aussi des améliorations sur les paires de langues suivantes: turc-anglais, allemand-anglais, chinois-anglais et tchèque-anglais. Dans notre deuxième article, nous proposons une approche pour aborder le problème des mots rares dans plusieurs tâches du traitement des langages. Notre approche modifie l’architecture habituelle des modèles encodeur-décodeur avec attention, en remplaçant la couche softmax du décodeur par notre couche pointer-softmax. Celle-ci permet au décodeur de pointer à différents endroits dans la phrase d’origine. Notre modèle apprend à alterner entre copier un mot de la phrase d’origine et prédire un mot provenant d’une courte liste de mots prédéfinie, de manière probabiliste. L’approche que nous avons proposée est entièrement entraînable par descente de gradient et n’utilise qu’un objectif de maximum de vraisemblance sur les tâches de traduction. Nous avons aussi montré que le pointer-softmax aide de manière significative aux tâches de traduction et de synthèse de documents. Dans notre article "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models", nous proposons deux approches pour apprendre l’alignement dans les modèles entraînés sur des séquences. Lorsque la longueur de l’entrée et celle de la sortie sont trop grandes, apprendre les alignements peut être très difficile. La raison est que lorsque le décodeur est trop puissant, il a tendance à ignorer l’alignement des mots pour ne se concentrer que sur le dernier mot de la séquence d’entrée. Nous avons proposé une nouvelle approche, inspirée d’un algorithme d’apprentissage par renforcement, en ajoutant explicitement un mécanisme de planification au décodeur. Ce nouveau mécanisme planifie à l’avance l’alignement pour les k prochaines prédictions. Notre modèle apprend également un plan de correction pour déterminer lorsqu’il est nécessaire de recalculer les alignements. Notre approche peut apprendre de haut niveaux d’abstraction au point de vue temporel et nous montrons que les alignements sont généralement de meilleure qualité. Nous obtenons également des gains de performance significatifs comparativement à notre modèle de référence, malgré le fait que nos modèles ont moins de paramètres et qu’ils aient été entraînés moins longtemps. Dans notre article "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes", nous proposons une nouvelle approche pour ajouter de manière explicite un mécanisme de mémoire aux réseaux de neurones. Contrairement aux RNNs conventionnels, la mémoire n’est pas seulement représentée au niveau des activations du réseau, mais également dans une mémoire externe. Notre modèle, D-NTM, utilise un mécanisme d’adressage plus simple que les Neural Turing Machine (NTM) en utilisant des paires clé-valeur. Nous montrons que les modèles disposant de ce nouveau mécanisme peuvent plus efficacement apprendre les dépendances à long terme, en plus de mieux généraliser. Nous obtenons des améliorations sur plusieurs tâches incluant entre autres la réponse aux questions sur bAbI, le raisonnement avec implication, MNIST permuté, ainsi que des tâches synthétiques. Dans notre article "Noisy Activation Functions", nous proposons une nouvelle fonction d’activation, qui rend les activations stochastiques en leur ajoutant du bruit. Notre motivation dans cet article est d’aborder les problèmes d’optimisation qui surviennent lorsque nous utilisons des fonctions d’activation qui saturent, comme celles généralement utilisées dans les RNNs. Notre approche permet d’utiliser des fonctions d’activation linéaires par morceaux sur les RNNs à porte. Nous montrons des améliorations pour un grand nombre de tâches sans effectuer de recherche d'hyper paramètres intensive. Nous montrons également que supprimer le bruit dans les fonctions d’activation a un profond impact sur l’optimisation.The goal of this thesis is to present some of the small steps taken on the path towards solving natural language understanding and learning long-term dependencies to develop artificial intelligence algorithms that can reason with language. This thesis is written as a thesis by articles and contains five articles. Each article in this thesis proposes a new model or algorithm and demonstrates the efficiency of the proposed approach to solve problems that involve long-term dependencies or require natural language understanding. Although some of the models are tested on a particular task (such as neural machine translation), the proposed methods in this thesis are generally applicable to other domains and tasks (and have been used in the literature). In the introduction of this thesis, we introduce some of the fundamental concepts behind training sequence models using neural networks. We first provide a brief introduction to neural networks and then dive into details of the some of approaches and algorithms that are used throughout this thesis. In our first article, we propose a novel method to utilize the abundant amount of available monolingual data for training neural machine translation models. We have accomplished this goal by first training a long short-term memory (LSTM) language model on a large monolingual corpus and then fusing the outputs or the hidden states of the LSTM language model with the decoder of the neural machine translation model. Our neural machine translation model is trained end to end with an attention mechanism. We have shown that our proposed approaches can improve the performance of the neural machine translation models significantly on the rare resource translation tasks and our approach improved the data-efficiency of the end to end neural machine translation systems. We report improvements on Turkish-English (Tr-En), German-English (De-En), Chinese-English (Zh-En) and Czech-English (Cz-En) translation tasks. In our second paper, we propose an approach to address the problem of rare words for natural language processing tasks. Our approach augments the encoder-decoder architecture with attention model by replacing the final softmax layer with our proposed pointer-softmax layer that creates pointers to the source sentences as the decoder translates. In the case of pointer-softmax, our model learns to switch between copying a word from the source and predicting a word from a shortlist vocabulary in a probabilistic manner. Our proposed approach is end-to-end trainable with a single maximum likelihood objective of the NMT model. We have also shown that it improves the performance of summarization and the neural machine translation model. We report significant improvements in machine translation and summarization tasks. In our "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models" paper, we propose two new approaches to learn alignments in a sequence to sequence model. If the input and the source context is very long, learning the alignments for a sequence to sequence model can be difficult. In particular, because when the decoder is a large network, it can learn to ignore the alignments and attend more on the last token of the input sequence. We propose a new approach which is inspired by a hierarchical reinforcement learning algorithm and extend our model with an explicit planning mechanism. The proposed alignment mechanism plans and computes the alignments for the next $k$ tokens in the decoder. Our model also learns a commitment plan to decide when to recompute the alignment matrix. Our proposed approach can learn high-level temporal abstractions, and we show that it qualitatively learns better alignments. We also achieve significant improvements over our baseline despite using smaller models and with less training. In "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes," we propose a new approach for augmenting neural networks with an explicit memory mechanism. As opposed to conventional RNNs, the memory is not only represented in the activations of the neural network but in an external memory that can be accessed via the neural network controller. Our model, D-NTM uses a more straightforward memory addressing mechanism than NTM which is achieved by using key-value pairs for each memory cell. We find out that the models augmented with an external memory mechanism can learn tasks that involve long-term dependencies more efficiently and achieve better generalization. We achieve improvements on many tasks including but not limited to episodic question answering on bAbI, reasoning with entailment, permuted MNIST task and synthetic tasks. In our "Noisy Activation Functions" paper, we propose a novel activation function that makes the activations stochastic by injecting a particular form of noise to them. Our motivation in this paper is to address the optimization problem of commonly used saturating activation functions that are used with the recurrent neural networks. Our approach enables us to use piece-wise linear activation functions on the gated recurrent neural network models. We show improvements in a wide range of tasks without doing any extensive hyperparameter search by a drop-in replacement. We also show that annealing the noise of the activation function can have a profound continuation-like effect on the optimization of the network

ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system