Methods › General › Self-Supervised Learning › PASE+

Problem Agnostic Speech Encoder +

PASE+

2 papers tagged archive 2025-07-28

Introduced by Mirco Ravanelli et al. in Multi-task self-supervised learning for Robust Speech Recognition

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

PASE+ is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Denoising1
Emotion Classification1
Multi-Task Learning1
Robust Speech Recognition1
Self-Supervised Learning1
Speech Denoising1
Speech Recognition1
speech-recognition1

Usage over time archive 2025-07-28

Papers per year tagged with PASE+: 2020 to 2020, peak 2 2 0 2020: 2 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Self-Supervised Learning

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