Abstract
Personalizing Automatic Speech Recognition (ASR) for non-normative speech remains challenging because data collection is labor-intensive and model training is technically complex. To address these limitations, we propose Adapt4Me, a web-based decentralized environment that operationalizes Bayesian active learning to enable end-to-end personalization without expert supervision. The app exposes data selection, adaptation, and validation to lay users through a three-stage human-in-the-loop workflow: (1) rapid profiling via greedy phoneme sampling to capture speaker-specific acoustics; (2) backend personalization using Variational Inference Low-Rank Adaptation (VI-LoRA) to enable fast, incremental updates; and (3) continuous improvement, where users guide model refinement by resolving visualized model uncertainty via low-friction top-k corrections. By making epistemic uncertainty explicit, Adapt4Me reframes data efficiency as an interactive design feature rather than a purely algorithmic concern. We show how this enables users to personalize robust ASR models, transforming them from passive data sources into active authors of their own assistive technology.
Abstract (translated)
针对非规范语音的个性化自动语音识别（ASR）仍面临挑战，因为数据收集耗时费力，模型训练技术复杂。为突破这些限制，我们提出了Adapt4Me——一个基于网络的去中心化环境，它将贝叶斯主动学习落地为可操作方案，实现无需专家监督的端到端个性化。该应用通过三阶段人机协同工作流，将数据选择、适配与验证向普通用户开放：(1) 通过贪婪音素采样进行快速特征提取，捕捉说话人特有的声学信息；(2) 后端采用变分推断低秩适配（VI-LoRA）实现快速增量更新；(3) 通过低摩擦度的top-k修正机制，让用户通过解决可视化模型不确定性来持续优化模型。Adapt4Me通过将认知不确定性显性化，将数据效率重新定义为交互式设计特性，而非纯算法问题。我们展示了这一方案如何让用户个性化鲁棒ASR模型，使他们从被动数据提供者转变为自身辅助技术的主动创造者。
URL
https://arxiv.org/abs/2603.20112
