PathBench: 自动病理语音评估的语音清晰度基准
PathBench: Speech Intelligibility Benchmark for Automatic Pathological Speech Assessment
AI总结：
提出PathBench基准，统一使用公开数据集比较无参考、参考文本和参考音频方法，并引入Dual-ASR发音精度（DArtP）方法，在无参考方法中取得最高平均相关性。
摘要：
自动语音清晰度评估对于监测语音障碍和治疗效果至关重要。然而，现有方法难以比较：研究分散在具有不一致协议的私有数据集上。我们引入了PathBench，一个使用公开数据集的病理语音评估统一基准。我们比较了无参考、参考文本和参考音频方法在三种协议（匹配内容、扩展和完整）下的表现，这些协议代表了语言学家（受控刺激）与机器学习专家（最大数据）处理相同数据的方式。我们在六个数据集上建立了基准基线，使得未来方法进展的系统评估成为可能，并引入了Dual-ASR发音精度（DArtP），在无参考方法中实现了最高平均相关性。
英文摘要：
Automatic speech intelligibility assessment is crucial for monitoring speech disorders and therapy efficacy. However, existing methods are difficult to compare: research is fragmented across private datasets with inconsistent protocols. We introduce PathBench, a unified benchmark for pathological speech assessment using public datasets. We compare reference-free, reference-text, and reference-audio methods across three protocols (Matched Content, Extended, and Full) representing how a linguist (controlled stimuli) versus machine learning specialist (maximum data) would approach the same data. We establish benchmark baselines across six datasets, enabling systematic evaluation of future methodological advances, and introduce Dual-ASR Articulatory Precision (DArtP), achieving the highest average correlation among reference-free methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
