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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 Page: 1 2 3 Hits 1 – 20 of 52 1 Differentially private speaker anonymization Shamsabadi, Ali Shahin; Srivastava, Brij Mohan Lal; Bellet, Aurélien... In: https://hal.inria.fr/hal-03588932 ; 2022 (2022) BASE Show details 2 The VoicePrivacy 2022 Challenge Evaluation Plan ... Tomashenko, Natalia; Wang, Xin; Miao, Xiaoxiao. - : arXiv, 2022 BASE Show details 3 Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition Sivasankaran, Sunit; Vincent, Emmanuel; Fohr, Dominique In: EUSIPCO 2020 - 28th European Signal Processing Conference ; https://hal.inria.fr/hal-02355669 ; EUSIPCO 2020 - 28th European Signal Processing Conference, Jan 2021, Amsterdam / Virtual, Netherlands. ⟨10.23919/Eusipco47968.2020.9287541⟩ ; https://eusipco2020.org/ (2021) BASE Show details 4 On Refining BERT Contextualized Embeddings using Semantic Lexicons Zervakis, Georgios; Vincent, Emmanuel; Couceiro, Miguel... In: Machine Learning with Symbolic Methods and Knowledge Graphs co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021) ; https://hal.archives-ouvertes.fr/hal-03318571 ; Machine Learning with Symbolic Methods and Knowledge Graphs co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Sep 2021, Online, Spain (2021) BASE Show details 5 Privacy and utility of x-vector based speaker anonymization Srivastava, Brij Mohan Lal; Maouche, Mohamed; Sahidullah, Md... In: https://hal.inria.fr/hal-03197376 ; 2021 (2021) BASE Show details 6 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 7 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 8 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 9 Transformer versus LSTM Language Models Trained on Uncertain ASR Hypotheses in Limited Data Scenarios Sheikh, Imran,; Vincent, Emmanuel; Illina, Irina In: https://hal.inria.fr/hal-03362828 ; 2021 (2021) BASE Show details 10 Enabling voice-based apps with European values Campbell, Akira; Kleinbauer, Thomas; Tommasi, Marc... In: ISSN: 0926-4981 ; ERCIM News ; https://hal.inria.fr/hal-03476390 ; ERCIM News, ERCIM, 2021, 126, pp.38-39 ; https://ercim-news.ercim.eu/images/stories/EN126/EN126-web.pdf (2021) BASE Show details 11 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 12 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 13 Enhancing Speech Privacy with Slicing Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: https://hal.inria.fr/hal-03369137 ; 2021 (2021) BASE Show details 14 Benchmarking and challenges in security and privacy for voice biometrics Bonastre, Jean-Francois; Delgado, Hector; Evans, Nicholas... In: SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication ; https://hal.archives-ouvertes.fr/hal-03346196 ; SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication, ISCA, Nov 2021, Magdeburg, Germany. ⟨10.21437/SPSC.2021-11⟩ ; https://spsc-symposium2021.de/#home (2021) BASE Show details 15 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 16 Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios Sheikh, Imran,; Vincent, Emmanuel; Illina, Irina In: https://hal.inria.fr/hal-03327306 ; 2021 (2021) BASE Show details 17 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 18 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 19 Adapting Language Models When Training on Privacy-Transformed Data Turan, Mehmet Ali Tugtekin; Klakow, Dietrich; Vincent, Emmanuel; Jouvet, Denis In: INTERSPEECH 2021 ; https://hal.inria.fr/hal-03189354 ; 2021 (2021) Abstract: Submitted to INTERSPEECH 2021 ; International audience ; In recent years, voice-controlled personal assistants have revolutionized the interaction with smart devices and mobile applications. These dialogue tools are then used by system providers to improve and retrain the language models (LMs). Each spoken message reveals personal information, hence, it is necessary to remove the private data from the input utterances. However, this may harm the LM training because privacy-transformed data is unlikely to match the test distribution. This paper aims to fill the gap by focusing on the adaptation of LM initially trained on privacy-transformed utterances. Our data sanitization process relies on named-entity recognition. We propose an LM adaptation strategy over the private data with minimum losses. Class-based modeling is an effective approach to overcome data sparsity in the context of n-gram model training. On the other hand, neural LMs can handle longer contexts which can yield better predictions. Our methodology combines the predictive power of class-based models and the generalization capability of neural models together. With privacy transformation, we have a relative 11% word error rate (WER) increase compared to an LM trained on the clean data. Despite the privacy-preserving, we can still achieve comparable accuracy. Empirical evaluations attain a relative WER improvement of 8% over the initial model. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; class-based language modeling; language model adaptation; privacy-preserving learning; speech recognition URL: https://hal.inria.fr/hal-03189354/file/Paper_1854.pdf https://hal.inria.fr/hal-03189354 https://hal.inria.fr/hal-03189354/document BASE Hide details 20 Privacy and utility of x-vector based speaker anonymization Srivastava, Brij Mohan Lal; Maouche, Mohamed; Sahidullah, Md... In: https://hal.inria.fr/hal-03197376 ; 2021 (2021) BASE Show details Page: 1 2 3

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