277 citations · 416 across the 28 of their papers we have counts for
31 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…
Semantic enrichment towards efficient speech representations
Gaëlle Laperrière, Ha Nguyen, Sahar Ghannay +2
Over the past few years, self-supervised learned speech representations have emerged as fruitful replacements for conventional surface representations when solving Spoken Language…
Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data
Jarod Duret, Titouan Parcollet, Yannick Estève
We propose a method for speech-to-speech emotionpreserving translation that operates at the level of discrete speech units. Our approach relies on the use of multilingual emotion e…
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset
Lucas Maison, Yannick Estève
Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning. However, they are known to…
OLISIA: a Cascade System for Spoken Dialogue State Tracking
Léo Jacqmin, Lucas Druart, Yannick Estève +3
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between…
Improving Accented Speech Recognition with Multi-Domain Training
Lucas Maison, Yannick Estève
Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lac…
Federated Learning for ASR based on Wav2vec 2.0
Tuan Nguyen, Salima Mdhaffar, Natalia Tomashenko +2
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2.0 model pre-trained by self supervision. Carried out on the well-known TED-L…
On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding
Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier +2
In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR mode…
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks
Marcely Zanon Boito, John Ortega, Hugo Riguidel +8
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speec…
End-to-end model for named entity recognition from speech without paired training data
Salima Mdhaffar, Jarod Duret, Titouan Parcollet +1
Recent works showed that end-to-end neural approaches tend to become very popular for spoken language understanding (SLU). Through the term end-to-end, one considers the use of a s…
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems
Marcely Zanon Boito, Laurent Besacier, Natalia Tomashenko +1
Self-supervised models for speech processing emerged recently as popular foundation blocks in speech processing pipelines. These models are pre-trained on unlabeled audio data and…
Speech Resources in the Tamasheq Language
Marcely Zanon Boito, Fethi Bougares, Florentin Barbier +4
In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource sp…
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…
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
Manon Macary, Marie Tahon, Yannick Estève +1
Pre-training for feature extraction is an increasingly studied approach to get better continuous representations of audio and text content. In the present work, we use wav2vec and…
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
Paul Tardy, David Janiszek, Yannick Estève +1
Summarizing texts is not a straightforward task. Before even considering text summarization, one should determine what kind of summary is expected. How much should the information…
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization
Paul Tardy, Louis de Seynes, François Hernandez +3
Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports ar…
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
Maha Elbayad, Ha Nguyen, Fethi Bougares +5
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and…
Exploring Gaussian mixture model framework for speaker adaptation of deep neural network acoustic models
Natalia Tomashenko, Yuri Khokhlov, Yannick Esteve
In this paper we investigate the GMM-derived (GMMD) features for adaptation of deep neural network (DNN) acoustic models. The adaptation of the DNN trained on GMMD features is done…