The selective use of gaze in automatic speech recognition

The performance of automatic speech recognition (ASR) degrades significantly in natural environments compared to in laboratory assessments. Being a major source of interference, acoustic noise affects speech intelligibility during the ASR process. There are two main problems caused by the acoustic noise. The first is the speech signal contamination. The second is the speakers' vocal and non-vocal behavioural changes. These phenomena elicit mismatch between the ASR training and recognition conditions, which leads to considerable performance degradation. To improve noise-robustness, exploiting prior knowledge of the acoustic noise in speech enhancement, feature extraction and recognition models are popular approaches. An alternative approach presented in this thesis is to introduce eye gaze as an extra modality. Eye gaze behaviours have roles in interaction and contain information about cognition and visual attention; not all behaviours are relevant to speech. Therefore, gaze behaviours are used selectively to improve ASR performance. This is achieved by inference procedures using noise-dependant models of gaze behaviours and their temporal and semantic relationship with speech. `Selective gaze-contingent ASR' systems are proposed and evaluated on a corpus of eye movement and related speech in different clean, noisy environments. The best performing systems utilise both acoustic and language model adaptation

De l'apprentissage faiblement supervisé au catalogage en ligne

Applied mathematics and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch from their old paradigms to machine learning. Interestingly, those data scientists spend more time scrapping, annotating and cleaning data than fine-tuning models. This thesis is motivated by the following question: can we derive a more generic framework than the one of supervised learning in order to learn from clutter data? This question is approached through the lens of weakly supervised learning, assuming that the bottleneck of data collection lies in annotation. We model weak supervision as giving, rather than a unique target, a set of target candidates. We argue that one should look for an “optimistic” function that matches most of the observations. This allows us to derive a principle to disambiguate partial labels. We also discuss the advantage to incorporate unsupervised learning techniques into our framework, in particular manifold regularization approached through diffusion techniques, for which we derived a new algorithm that scales better with input dimension then the baseline method. Finally, we switch from passive to active weakly supervised learning, introducing the “active labeling” framework, in which a practitioner can query weak information about chosen data. Among others, we leverage the fact that one does not need full information to access stochastic gradients and perform stochastic gradient descent.Les mathématiques appliquées et le calcul nourrissent beaucoup d’espoirs à la suite des succès récents de l’apprentissage supervisé. Dans l’industrie, beaucoup d’ingénieurs cherchent à remplacer leurs anciens paradigmes de pensée par l’apprentissage machine. Étonnamment, ces ingénieurs passent plus de temps à collecter, annoter et nettoyer des données qu’à raffiner des modèles. Ce phénomène motive la problématique de cette thèse: peut-on définir un cadre théorique plus général que l’apprentissage supervisé pour apprendre grâce à des données hétérogènes? Cette question est abordée via le concept de supervision faible, faisant l’hypothèse que le problème que posent les données est leur annotation. On modélise la supervision faible comme l’accès, pour une entrée donnée, non pas d’une sortie claire, mais d’un ensemble de sorties potentielles. On plaide pour l’adoption d’une perspective « optimiste » et l’apprentissage d’une fonction qui vérifie la plupart des observations. Cette perspective nous permet de définir un principe pour lever l’ambiguïté des informations faibles. On discute également de l’importance d’incorporer des techniques sans supervision d’appréhension des données d’entrée dans notre théorie, en particulier de compréhension de la variété sous-jacente via des techniques de diffusion, pour lesquelles on propose un algorithme réaliste afin d’éviter le fléau de la dimension, à l’inverse de ce qui existait jusqu’alors. Enfin, nous nous attaquons à la question de collecte active d’informations faibles, définissant le problème de « catalogage en ligne », où un intendant doit acquérir une maximum d’informations fiables sur ses données sous une contrainte de budget. Entre autres, nous tirons parti du fait que pour obtenir un gradient stochastique et effectuer une descente de gradient, il n’y a pas besoin de supervision totale

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research