118 citations · 246 across the 17 of their papers we have counts for
17 papers
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Titouan Parcollet, Ha Nguyen, Solene Evain +19
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing…
Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
Riccardo Presotto, Sannara Ek, Gabriele Civitarese +3
The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts…
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones
Sannara Ek, François Portet, Philippe Lalanda +1
Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying o…
Cross-domain Voice Activity Detection with Self-Supervised Representations
Sina Alisamir, Fabien Ringeval, Francois Portet
Voice Activity Detection (VAD) aims at detecting speech segments on an audio signal, which is a necessary first step for many today's speech based applications. Current state-of-th…
Dynamic Time-Alignment of Dimensional Annotations of Emotion using Recurrent Neural Networks
Sina Alisamir, Fabien Ringeval, Francois Portet
Most automatic emotion recognition systems exploit time-continuous annotations of emotion to provide fine-grained descriptions of spontaneous expressions as observed in real-life i…
Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition
Sannara EK, François Portet, Philippe Lalanda
Human Activity Recognition (HAR) on mobile devices has been demonstrated to be possible using neural models trained on data collected from the device's inertial measurement units.…
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR
Sannara Ek, Romain Rombourg, François Portet +1
Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that train…
Federated Continual Learning through distillation in pervasive computing
Anastasiia Usmanova, François Portet, Philippe Lalanda +1
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on d…
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain
Anastasiia Usmanova, François Portet, Philippe Lalanda +1
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on d…
End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting
Thierry Desot, François Portet, Michel Vacher
Spoken Language Understanding (SLU) is a core task in most human-machine interaction systems. With the emergence of smart homes, smart phones and smart speakers, SLU has become a k…
Toward Low-Cost End-to-End Spoken Language Understanding
Marco Dinarelli, Marco Naguib, François Portet
Recent advances in spoken language understanding benefited from Self-Supervised models trained on large speech corpora. For French, the LeBenchmark project has made such models ava…
Vers la compréhension automatique de la parole bout-en-bout à moindre effort
Marco Naguib, François Portet, Marco Dinarelli
Recent advances in spoken language understanding benefited from Self-Supervised models trained on large speech corpora. For French, the LeBenchmark project has made such models ava…
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison
Sannara Ek, François Portet, Philippe Lalanda +1
Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recent…
Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings
Ali Can Kocabiyikoglu, François Portet, Raheel Qader +1
Drug prescriptions are essential information that must be encoded in electronic medical records. However, much of this information is hidden within free-text reports. This is why t…
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
Solene Evain, Ha Nguyen, Hang Le +15
Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. T…
Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
Raheel Qader, François Portet, Cyril Labbé
In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of careful…
Arcades: A deep model for adaptive decision making in voice controlled smart-home
Alexis Brenon, François Portet, Michel Vacher
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which…