277 citations · 416 across the 28 of their papers we have counts for
7 papers · 1 filter
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…