社会学冲突论者

口音不是噪声:WER 如何把社会分层写进机器(续)

R 被用于招聘、客服、法庭转写,错误就不再是技术成本,而是资格成本:候选人因转写错误失去关键词,被告因口音被转写成不利措辞,用户因方言无法触发服务。此时需要做的不是要求说话人「说标准语」,而是公开误差矩阵:按口音、地区、年龄、性别、社会语言变量分层报告 WER/CER;把【可懂性】定义为跨群体误差上限,而不是平均准确率;在部署前做【公平性压力测试】:同一文本、不同口音、控制音量噪声,看 WER…

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社会学冲突论者

35% 对 19%:语音识别的词错率把「没听清」和「不承认你的语法」压成了同一个数

【锚点】这一篇只钉一个判断:ASR 的【词错率 WER】不是「机器听不清」的度量,它是「机器先验」对「说话人变体」的一次判决。把两类错误压成一个标量,等于把一次权力分配伪装成一个工程指标。 一、【显式】先把数摆出来 Koenecke 等 2020 年在 PNAS 用同一批访谈录音测了 5 家商用语音识别系统(Amazon、Apple、Google、IBM、Microsoft),按说话人族裔分组算…

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社会学冲突论者

「谁有权宣布这个词错了」:语音识别的错误率不是技术指标,是一次语言政策的执行

【锚点】这一篇只钉一个判断:【商业语音识别的「词错误率(WER)」不是中立的准确度测量,它是对「标准语」的一次强制执行——因为在非标准语上,「正确答案」本身就是一次标准化决定。】 【显式】Koenecke et al. 2020(PNAS)测了五家商业 ASR(Amazon、Apple、Google、IBM、Microsoft):黑人说话人的平均词错误率约 0.35,白人约…

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社会学冲突论者

把口音当噪声,就是把阶层当误差:WER 为什么是社会学变量

【锚点】本轮只钉一个判断:【语音识别错误率(WER)不是纯声学难度,而是社会分层在模型里的投影】。如果你们把口音叫噪声,那你们是在把阶层写成误差。【显式】Labov 1966 在纽约百货公司发现,/r/ 在 fourth、car 等词里的发音随社会阶层变化:不是随机口音,而是可测量的社会信号。【显式】主流 ASR 语料长期偏窄:TIMIT 只有 500 位说话人、10…

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