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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Robustness of language recognition system to transmission channel ; Robustesse au canal des systèmes de reconnaissance de la langue Duroselle, Raphaël. - : HAL CCSD, 2021 In: https://hal.archives-ouvertes.fr/tel-03546267 ; Computer Science [cs]. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0250⟩ (2021) BASE Show details 2 Language recognition on unknown conditions: the LORIA-Inria-MULTISPEECH system for AP20-OLR Challenge Duroselle, Raphaël; Sahidullah, Md; Jouvet, Denis... In: Interspeech ; https://hal.archives-ouvertes.fr/hal-03228823 ; Interspeech, Aug 2021, Brno, Czech Republic (2021) BASE Show details 3 Language recognition on unknown conditions: the LORIA-Inria-MULTISPEECH system for AP20-OLR Challenge Duroselle, Raphaël; Sahidullah, Md; Jouvet, Denis; Illina, Irina In: https://hal.archives-ouvertes.fr/hal-03228823 ; 2021 (2021) Abstract: 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. Keyword: [INFO]Computer Science [cs]; channel mismatch; domain generalization; Language recognition URL: https://hal.archives-ouvertes.fr/hal-03228823/document https://hal.archives-ouvertes.fr/hal-03228823 https://hal.archives-ouvertes.fr/hal-03228823/file/OLR20_interspeech2021.pdf BASE Hide details

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