InterCorrect：面向公平ASR的人口统计模型合并的交叉感知校正
InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR
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中文总结 AI 辅助
本研究提出InterCorrect方法，通过人口统计感知的模型合并与交叉校正向量，改善基于语音-LLM的ASR系统在多个群体上的公平性，实验将整体WER从7.38%降至5.13%。
中文摘要 AI 辅助
自动语音识别（ASR）系统在不同人口统计群体上的表现往往不均衡，对于属于多个人口统计群体的说话者，错误尤其难以解决。本研究针对基于语音-LLM的公平ASR，探讨了人口统计感知的模型合并方法。从SLAM-ASR模型出发，我们仅对连接器在人口统计特定子集上进行微调，并将得到的子组适应连接器合并为全局模型。随后，我们利用子组词错误率（WER）和任务向量冲突识别关键的跨轴人口统计对，并对全局合并模型应用交叉特定校正向量。在Fair-Speech上的实验表明，全局人口统计合并相比基础模型改善了整体WER，而交叉校正为多种合并策略带来了额外增益。特别是，TIES结合基于WER的校正取得了最佳的整体WER，将其从7.38%降至5.13%。子组和差异分析进一步表明，所提出的方法改善了各人口统计轴上的性能，同时强调较低的平均WER并不总是意味着子组差异的减少。
英文摘要
Automatic Speech Recognition (ASR) systems often show uneven performance across demographic groups, and errors can be especially difficult to address for speakers belonging to multiple demographic groups. This work studies demographic-aware model merging for fair Speech-LLM-based ASR. Starting from a SLAM-ASR-based model, we fine-tune only the connector on demographic-specific subsets and merge the resulting subgroup-adapted connectors into a global model. We then identify critical cross-axis demographic pairs using subgroup WER and task-vector conflict, and apply intersection-specific correction vectors to the global merged model. Experiments on Fair-Speech show that global demographic merging improves overall WER over the base model, while intersection correction provides additional gains for several merging strategies. In particular, TIES with WER-based correction achieves the best overall WER, reducing it from 7.38\% to 5.13\%. Subgroup and disparity analyses further show that the proposed approach improves performance across demographic axes, while highlighting that lower average WER does not always imply reduced subgroup disparity.
发表机构
- PUCP（秘鲁天主教大学）
- University of Essex（埃塞克斯大学）
- JKU Linz（林茨约翰·开普勒大学）
机构由 AI 辅助整理，请以论文原文为准。
