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1. Language recognition on unknown conditions: the LORIA-Inria-MULTISPEECH system for AP20-OLR Challenge

Author

Irina Illina, Raphaël Duroselle, Sahidullah, Denis Jouvet, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Experiments presented in this paper were carried out using the Grid5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). This work has been partly funded by the French Direction Genérale de l’Armement., Grid'5000, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Channel (digital image), Computer science, Generalization, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Regularization (mathematics), Bottleneck, Domain (software engineering), Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, Robustness (computer science), Language recognition, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], domain generalization, 0305 other medical science, Selection (genetic algorithm), channel mismatch
Abstract

International audience; We describe the LORIA-Inria-MULTISPEECH system submitted to the Oriental Language Recognition AP20-OLR Challenge. This system has been specifically designed to be robust to unknown conditions: channel mismatch (task 1) and noisy conditions (task 3). Three sets of studies have been carried out for elaborating the system: design of multilingual bottleneck features, selection of robust features by evaluating language recognition performance on an unobserved channel, and design of the final models with different loss functions which exploit channel diversity within the training set. Key factors for robustness to unknown conditions are data augmentation techniques, stochastic weight averaging, and regularization of TDNNs with domain robustness loss functions. The final system is the combination of four TDNNs using bottleneck features and one GMM using SDC-MFCC features. Within the AP20-OLR Challenge, it achieves the top performance for tasks 1 and 3 with a $C_{avg}$ of respectively 0.0239 and 0.0374. This validates the approach for generalization to unknown conditions.

Published
2021

2. Efficiency of Correlation in a Bottleneck Game

Author

Marco Scarsini, Thomas J Rivera, Tristan Tomala, Orange Labs, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), and HEC Paris Research Paper Series

Subjects
TheoryofComputation_MISCELLANEOUS, price of anarchy, Computer Science::Computer Science and Game Theory, Correlated equilibrium, Mathematical optimization, Computer science, price of correlated stability, Nash equilibrium, price of stability, Bottleneck, correlated equilibrium, Task (project management), symbols.namesake, Discrete time and continuous time, symbols, Price of anarchy, [SHS.GESTION]Humanities and Social Sciences/Business administration, Train, efficiency of equilibria, Price of stability
Abstract

We consider a model of bottleneck congestion in discrete time with a penalty cost for being late. This model can be applied to several situations where agents need to use a capacitated facility in order to complete a task before a hard deadline. A possible example is a situation where commuters use a train service to go from home to office in the early morning. Trains run at regular intervals, take always the same time to cover their itinerary, and have a fixed capacity. Commuters must reach their office in time. This is a hard constraint whose violation involves a heavy penalty. Conditionally on meeting the deadline, commuters want to take the train as late as possible. With the intent of considering strategic choices of departure, we model this situation as a game and we show that it does not have pure Nash equilibria. Then we characterize the best and worst mixed Nash equilibria, and show that they are both inefficient with respect to the social optimum. We then show that there exists a correlated equilibrium that approximates the social optimum when the penalty for missing the deadline is sufficiently large.

Published
2018