2 research outputs found
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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. They were notably successful to improve
performance on downstream tasks such as automatic speech recognition (ASR).
While these works suggest it is possible to reduce dependence on labeled data
for building efficient speech systems, their evaluation was mostly made on ASR
and using multiple and heterogeneous experimental settings (most of them for
English). This questions the objective comparison of SSL approaches and the
evaluation of their impact on building speech systems. In this paper, we
propose LeBenchmark: a reproducible framework for assessing SSL from speech. It
not only includes ASR (high and low resource) tasks but also spoken language
understanding, speech translation and emotion recognition. We also focus on
speech technologies in a language different than English: French. SSL models of
different sizes are trained from carefully sourced and documented datasets.
Experiments show that SSL is beneficial for most but not all tasks which
confirms the need for exhaustive and reliable benchmarks to evaluate its real
impact. LeBenchmark is shared with the scientific community for reproducible
research in SSL from speech.Comment: Will be presented at Interspeech 202
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Self-supervised learning (SSL) is at the origin of unprecedented improvements
in many different domains including computer vision and natural language
processing. Speech processing drastically benefitted from SSL as most of the
current domain-related tasks are now being approached with pre-trained models.
This work introduces LeBenchmark 2.0 an open-source framework for assessing and
building SSL-equipped French speech technologies. It includes documented,
large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous
speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to
one billion learnable parameters shared with the community, and an evaluation
protocol made of six downstream tasks to complement existing benchmarks.
LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for
speech with the investigation of frozen versus fine-tuned downstream models,
task-agnostic versus task-specific pre-trained models as well as a discussion
on the carbon footprint of large-scale model training.Comment: Under submission at Computer Science and Language. Preprint allowe