让用户零门槛个性化语音识别，靠智能纠错提升识别准确率。
Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speech
- 通过用户引导的三阶段流程，实现非标准语音的自动适配。
- 利用变分推断低秩适配技术，实现快速增量更新。
- 可视化模型不确定性，让用户轻松修正错误提升性能。
为非标准语音个性化自动语音识别（ASR）仍面临数据收集耗时、模型训练复杂等挑战。为此，我们提出 Adapt4Me，一个基于网页的去中心化环境，通过贝叶斯主动学习实现端到端个性化，无需专家监督。该应用通过三阶段人机协同工作流将数据选择、模型适配与验证开放给普通用户：(1) 通过贪心音素采样快速完成用户声学特征建模；(2) 后端使用变分推断低秩适配（VI-LoRA）实现快速、增量式更新；(3) 连续优化阶段，用户通过低摩擦的 top-k 修正方式，根据可视化模型不确定性指导模型改进。通过显式呈现认知不确定性，Adapt4Me 将数据效率转化为可交互的设计特性，使用户从被动数据提供者转变为自身辅助技术的主动创作者。实验证明该方法可有效构建鲁棒的个性化 ASR 模型。
原文摘要 · Abstract (English)
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.
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