The paper examines how different data‑augmentation techniques affect low‑resource automatic speech recognition beyond overall error rates. It compares speed perturbation, SpecAugment, and two text‑based audio synthesis methods across four low‑resource languages and English, analyzing phoneme‑level errors. The study finds that SpecAugment improves aggregate performance but increases tone and palatalization errors, while text‑based methods introduce consonant insertions; when combined, each method mitigates the other's weaknesses, yielding superadditive gains that diminish as data size grows to 100 hours.
By Katsumi Ibaraki, David Chiang
arXiv:2606. 14391v1 Announce Type: cross Abstract: Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, leading to information loss and hallucinations.
By Henri-Leon Kordt, Theresa Pekarek Rosin, Jae Hee Lee, Stefan Wermter
arXiv:2606. 26629v1 Announce Type: new Abstract: Weight-space regularization methods such as Elastic Weight Consolidation (EWC) are the standard approach to catastrophic forgetting in continual learning.
By Evan Ning, Wei Xue, Dong Lou, Yike Guo
We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory. md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best.
arXiv:2607. 26410v1 Announce Type: cross Abstract: We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.
By Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko, Zhehuai Chen, Jagadeesh Balam, Boris Ginsburg
arXiv:2608. 15901v1 Announce Type: cross Abstract: Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically diagonal Fisher values.
By Brian B. Moser, Ahmed Anwar, Tobias Christian Nauen, Shishir Muralidhara, Federico Raue, Ren\'e Schuster, Stanislav Frolov, Andreas Dengel
arXiv:2606. 14459v1 Announce Type: cross Abstract: Modern Automatic Speech Recognition (ASR) systems have made remarkable progress on standard benchmarks, yet performance gaps have emerged under real-world distribution shifts, caused by recording conditions, accents, speech impairments, and noise.
By Theresa Pekarek Rosin, Matthias Kerzel, Stefan Wermter
arXiv:2601. 13844v3 Announce Type: replace Abstract: We study generalization in an overparameterized continual linear regression setting, where a model is trained with L2 (isotropic) regularization across a sequence of tasks.
By Gilad Karpel, Edward Moroshko, Ran Levinstein, Ron Meir, Daniel Soudry, Itay Evron
The study investigates whether a prosody‑trained representation can improve automatic speech recognition beyond the effect of a trainable fusion mechanism. Using a frozen HuBERT backbone and a 64‑dimensional prosodic representation, the authors compare a baseline recognizer, a fusion model with no auxiliary input, and a fusion model with the learned representation across Buckeye, Switchboard, and AMI IHM datasets. While the fusion model without auxiliary input reduces WER relative to the baseline, adding the prosodic representation yields no significant WER improvement, though the model still depends on the representation for optimal performance.
By Ki Woong Moon, Daniel Brenner
arXiv:2610.09835v1 Announce Type: new
Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using o...
By Jonghyun Han, Younghoon Song, Jongyoul Park
arXiv:2606. 10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored.
By Jingyi Xie, Yijun Lin, Yinjiang Xiong, Zhikun Zhang, Sai Li
The paper introduces EoupCT, a framework that estimates and orthogonalizes unknown pre‑training gradients to mitigate catastrophic forgetting during continual fine‑tuning of large language models. It generates pseudo data most susceptible to forgetting using a learnable soft prompt with Gumbel‑Softmax, then jointly optimizes model parameters and the prompt via a first‑order Pareto optimizer to enforce orthogonality between new task updates and the estimated gradients. Experiments on multiple LLMs show that EoupCT preserves both task‑specific performance and the models’ inherent general‑purpose knowledge.
By Bing Wang, Changchun Li, Xin-Qiang Cai, Lin Yuanbo Wu, Ximing Li, Gang Niu, Masashi Sugiyama