Category: Seminars

Formant shifting for speech intelligibility improvement in car noise environment

Speaker: Karan Nathwani (post-doctoral fellow) Date: June 9, 2016 Abstract: In this work, we propose a novel approach aiming at improving the intelligibility of speech in the context of in-car applications. Speech produced in noisy environments is subject to the Lombard effect which gathers a number of voice transformation effects compared to the speech produced in calm …

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A step towards multidimensional automatic improvisation

Speaker: Ken Déguernel (PhD student) Date: June 2, 2016 Abstract: Automatic music improvisation systems based on the OMax paradigm use training over a one-dimensional sequence to generate original improvisation. First, we propose a system creating improvisation in a closer way to a human improviser where the intuition of a context is enriched with knowledge. This system combines …

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A combined evaluation of established and new approaches for speech recognition in varied reverberation conditions

Speaker: Sunit Sivasankaran (Engineer) Date: May 12, 2016 Abstract: Robustness to reverberation is a key concern for distant-microphone ASR. Various approaches have been proposed, including single-channel or multichannel dereverberation, robust feature extraction, alternative acoustic models, and acoustic model adaptation. We conduct a series of experiments to assess the impact of various dereverberation and acoustic model adaptation approaches on the ASR …

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Optimal transport for domain adaptation

Speaker: Alain Rakotomamonjy (Université de Rouen) Date: May 11, 2016 Abstract: Domain adaptation addresses one of the most challenging tasks in machine learning : coping with mismatch between learning and testing probability distributions. If adaptation is done correctly, models learned on a specific data representation become more robust when confronted to data depicting the same problems, but described through another …

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Compact Multiview Representation of Documents Based on the Total Variability Space

Speaker: Mohamed Bouallegue (post-doctoral fellow) Date: April 21, 2016 Abstract: In this talk, I present my research work during my thesis at Laboratoire Informatique d’Avignon and my postdoctoral research at Laboratoire d’Informatique de l’Université du Maine. This work explores the paradigm of Factor Analysis/i-vector for identification of topics in spoken documents. We identify themes from dialogues of …

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Introduction to Sum Product Networks for noisy speech recognition

Speaker: Juan Andrés Morales Cordovilla (post-doctoral fellow) Date: March 3, 2016 Abstract: Sum Product Networks (SPN) are a new kind of probabilistic models that have the advantages of Deep learning of Neural Networks (DNNs) and of exact marginalization of Gaussian Mixture Models (GMMs). These two properties are very useful to do Missing Data or Uncertainty Decoding on the …

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Weakly supervised discriminative training of linear models for natural language processing

Speaker: Christophe Cerisara (Synalp team) Date: February 4, 2016 Abstract: This talk explores weakly supervised training of discriminative linear classifiers. Such features-rich classifiers have been widely adopted by the scientific community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. However, …

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Global modeling of speech production for articulatory synthesis

Speaker: Benjamin Elie (post-doctoral fellow) Date: January 21, 2016 Abstract: Articulatory synthesis consists in the numerical simulation of the articulatory, mechanical and acoustic phenomena involved in speech production. Unlike the concatenative approach, it enables these phenomena to be investigated, the speech signal to be specifically designed by virtually modifying the physiological parameters of the speaker, and the …

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Is audio signal processing still useful in the era of machine learning?

Speaker: Emmanuel Vincent Date: January 14, 2016 Abstract: Audio signal processing has long been the obvious approach to problems such as microphone array processing, active noise control, or speech enhancement. Yet, it is increasingly being challenged by black-box machine learning approaches based on, e.g., deep neural networks (DNN), which have already achieved superior results on certain tasks. …

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Detecting social attention attractors in free-standing conversational groups through multimodal head and body pose estimation

Speaker: Xavier Alameda-Pineda Date: December 3, 2015 Abstract: During natural social gatherings, humans tend to organize themselves in the so-called free-standing conversational groups (FCGs). Studying FCGs in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioural and personality traits) …

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