Methods › Audio › Speech Recognition › IPL

Iterative Pseudo-Labeling

IPL

17 papers tagged archive 2025-07-28

Introduced by Qiantong Xu et al. in Iterative Pseudo-Labeling for Speech Recognition

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

Iterative Pseudo-Labeling (IPL) is a semi-supervised algorithm for speech recognition which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. In particular, IPL fine tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.

PaperSource

Papers archive 2025-07-28

17 shown of 17, 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

20 shown of 33 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
Automatic Speech Recognition2
Automatic Speech Recognition (ASR)2
Language Modeling2
Language Modelling2
Speech Recognition2
speech-recognition2
Action Recognition1
BIG-bench Machine Learning1
Collaborative Filtering1
Computational Efficiency1
Data Augmentation1
Diversity1
Hallucination1
Image Generation1
Image Segmentation1
Management1
Medical Image Analysis1
Medical Image Segmentation1
Object1
Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with IPL: 2020 to 2024, peak 6 6 0 2020: 2 papers 2020 2021: 4 papers 2021 2022: 1 paper 2022 2023: 6 papers 2023 2024: 4 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (17 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

Speech RecognitionSemi-Supervised Learning Methods

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