The paper presents a practical approach to semi‑supervised federated learning for automatic speech recognition (ASR). It demonstrates that using a per‑client online teacher combined with a stabilizing server‑side anchor—where the server continues training on labeled data between rounds—significantly reduces divergence caused by pseudo‑label errors. The authors provide design guidelines that improve in‑domain performance by an average of 20.8 % and cross‑domain performance by 10.0 % over the best prior method, narrowing the gap to fully‑supervised federated learning.
By Wonho Bae, Zakaria Aldeneh, Martin Pelikan, Jan "Honza" Silovsky, Tatiana Likhomanenko, Sheikh Shams Azam
arXiv:2607. 03068v1 Announce Type: cross Abstract: Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful confidence filtering.
By Ebenezer Tarubinga
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
By Jinshi Liu, Lei He, Pan Liu
arXiv:2607. 15467v1 Announce Type: new Abstract: Knowledge distillation enables an adversary to replicate a proprietary classifier by querying its prediction interface and training a surrogate on the returned probability vectors.
By Khawaja Abaid Ullah, Mohammad Javad Khojasteh
arXiv:2602. 19778v4 Announce Type: replace-cross Abstract: Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord labels, as well-aligned annotations are costly to acquire.
By Nghia Phan, Rong Jin, Gang Liu, Xiao Dong
arXiv:2407. 05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data.
By Zeju Li, Ying-Qiu Zheng, Chen Chen, Saad Jbabdi