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Santos M.A.,I3A | Villalon J.,I3A | Ramirez-Mireles F.,Engineering Division at ITAM | Orozco-Barbosa L.,I3A | Delicado J.,I3A
IEEE Communications Letters | Year: 2011

In this letter, we introduce a novel Multicast Collision Prevention (MCP) mechanism for IEEE 802.11 WLANs. The proposed MCP mechanism has been designed following the operation principles of the IEEE 802.11 standard. Our analysis shows that the proposed MCP mechanism effectively improves the reliability and efficiency of the multicast mechanism defined by the IEEE 802.11 standard. Simulation results also show that our proposal properly interoperates with the 802.11 unicast mechanism. © 2011 IEEE.

Lopez-Moreno I.,Google | Gonzalez-Dominguez J.,Autonomous University of Madrid | Martinez D.,I3A | Plchot O.,Brno University of Technology | And 2 more authors.
Computer Speech and Language | Year: 2016

In this work, we present a comprehensive study on the use of deep neural networks (DNNs) for automatic language identification (LID). Motivated by the recent success of using DNNs in acoustic modeling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from its short-term acoustic features. We propose two different DNN-based approaches. In the first one, the DNN acts as an end-to-end LID classifier, receiving as input the speech features and providing as output the estimated probabilities of the target languages. In the second approach, the DNN is used to extract bottleneck features that are then used as inputs for a state-of-the-art i-vector system. Experiments are conducted in two different scenarios: the complete NIST Language Recognition Evaluation dataset 2009 (LRE'09) and a subset of the Voice of America (VOA) data from LRE'09, in which all languages have the same amount of training data. Results for both datasets demonstrate that the DNN-based systems significantly outperform a state-of-art i-vector system when dealing with short-duration utterances. Furthermore, the combination of the DNN-based and the classical i-vector system leads to additional performance improvements (up to 45% of relative improvement in both EER and Cavg on 3s and 10s conditions, respectively). © 2016 The Authors. Published by Elsevier Ltd.

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