SUPERB
Self-supervised speech models are tested as frozen encoders on a range of speech tasks, including speech recognition, keyword spotting, speaker identification, intent classification and emotion recognition. Each task trains only a light prediction head on top of the shared model.
SUPERB scores speech encoders such as wav2vec 2.0, HuBERT and WavLM, not end-to-end voice assistants. The 2,568 GitHub stars belong to the S3PRL toolkit that runs it. Each task keeps its own metric, so sources often report a few tasks, not one number.
- Made by
- National Taiwan University and collaborators
- Released
- May 2021
- Kind
- Speech recognition and translation
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Short answer
- Tasks
- All public
In the authors’ words
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing.
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for SUPERB yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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