回归基础：在语音助手时代重新审视ASR
Back to Basics: Revisiting ASR in the Age of Voice Agents
AI总结：
研究指出现有ASR系统在真实场景下表现不佳，提出多语言诊断基准WildASR，揭示模型在环境退化、人口变化和语言多样性下的性能退化问题，并提供分析工具提升可靠性。
摘要：
自动语音识别（ASR）系统在经过筛选的基准测试中已接近人类准确率，但在真实世界的语音助手应用中仍面临当前评估未系统覆盖的条件挑战。缺乏能隔离特定失败因素的诊断工具，使从业者无法预知哪些条件、语言会导致何种程度的退化。我们引入了多语言（四种语言）的诊断基准WildASR，完全基于真实人类语音，沿环境退化、人口变化和语言多样性三个轴分解ASR鲁棒性。评估七种广泛使用的ASR系统后，发现严重的不均匀性能退化，且模型鲁棒性无法跨语言或条件转移。关键发现是，模型在部分或退化的输入下常生成合理但未说出的内容，对下游助手行为构成实际安全风险。我们的结果表明，针对因素隔离的评估对于理解并改进生产系统中的ASR可靠性至关重要。除了基准本身，我们还提出了三种分析工具，供从业者用于指导部署决策。
英文摘要：
Automatic speech recognition (ASR) systems have achieved near-human accuracy on curated benchmarks, yet still fail in real-world voice agents under conditions that current evaluations do not systematically cover. Without diagnostic tools that isolate specific failure factors, practitioners cannot anticipate which conditions, in which languages, will cause what degree of degradation. We introduce WildASR, a multilingual (four-language) diagnostic benchmark sourced entirely from real human speech that factorizes ASR robustness along three axes: environmental degradation, demographic shift, and linguistic diversity. Evaluating seven widely used ASR systems, we find severe and uneven performance degradation, and model robustness does not transfer across languages or conditions. Critically, models often hallucinate plausible but unspoken content under partial or degraded inputs, creating concrete safety risks for downstream agent behavior. Our results demonstrate that targeted, factor-isolated evaluation is essential for understanding and improving ASR reliability in production systems. Besides the benchmark itself, we also present three analytical tools that practitioners can use to guide deployment decisions.
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。
