277 citations · 416 across the 28 of their papers we have counts for
7 papers · 1 filter
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition
Salima Mdhaffar, Jean-François Bonastre, Marc Tommasi +2
The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to par…
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi +2
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recogniti…
Where are we in semantic concept extraction for Spoken Language Understanding?
Sahar Ghannay, Antoine Caubrière, Salima Mdhaffar +3
Spoken language understanding (SLU) topic has seen a lot of progress these last three years, with the emergence of end-to-end neural approaches. Spoken language understanding refer…
Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
Ha Nguyen, Yannick Estève, Laurent Besacier
Boosted by the simultaneous translation shared task at IWSLT 2020, promising end-to-end online speech translation approaches were recently proposed. They consist in incrementally e…
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…
An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies
Ha Nguyen, Yannick Estève, Laurent Besacier
This paper proposes a decoding strategy for end-to-end simultaneous speech translation. We leverage end-to-end models trained in offline mode and conduct an empirical study for two…
End2End Acoustic to Semantic Transduction
Valentin Pelloin, Nathalie Camelin, Antoine Laurent +4
In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism. It reliably selects contextual acoustic features…