Speech perception, like all perception, takes place in context. Recognition of a given speech sound is influenced by the acoustic properties of surrounding sounds. When the spectral composition of earlier (context) sounds (e.g., a sentence with more energy at lower third formant [F₃] frequencies) differs from that of a later (target) sound (e.g., consonant with intermediate F₃ onset frequency), the auditory system magnifies this difference, biasing target categorization (e.g., towards higher-F₃-onset /d/). Historically, these studies used filters to force context stimuli to possess certain spectral compositions. Recently, these effects were produced using unfiltered context sounds that already possessed the desired spectral compositions (Stilp & Assgari, 2019, Attention, Perception, & Psychophysics, 81, 2037–2052). Here, this natural signal statistics approach is extended to consonant categorization (/g/–/d/). Context sentences were either unfiltered (already possessing the desired spectral composition) or filtered (to imbue specific spectral characteristics). Long-term spectral characteristics of unfiltered contexts were poor predictors of shifts in consonant categorization, but short-term characteristics (last 475 ms) were excellent predictors. This diverges from vowel data, where long-term and shorter-term intervals (last 1,000 ms) were equally strong predictors. Thus, time scale plays a critical role in how listeners attune to signal statistics in the acoustic environment.

语音感知与所有感知一样，都是在上下文中发生的。给定语音的识别受周围声音的声学特性影响。当较早的（上下文）声音的频谱组成（例如，在较低的第三共振峰[F ₃ ]频率处具有更多能量的句子）与较晚的（目标）声音（例如，具有中等F ₃起始频率的辅音）的频谱组成不同时，听觉系统会放大这种差异，使目标分类偏向（例如，朝向较高F ₃-set / d /）。从历史上看，这些研究使用过滤器来强制情境刺激具有某些光谱成分。最近，这些效果是使用已经具有所需频谱成分的未经过滤的上下文声音产生的（Stilp＆Assgari，2019; Attention，Perception，＆Psychophysics，81，2037–2052）。在这里，这种自然信号统计方法扩展到了辅音分类（/ g / – / d /）。上下文句子要么未经过滤（已经拥有所需的频谱组成），要么经过过滤（赋予特定的频谱特征）。未过滤上下文的长期频谱特征不能很好地预测辅音分类的变化，但是短期特征（最后475毫秒）是出色的预测因素。这与元音数据不同，在元音数据中，长期间隔和短期间隔（最后1,000毫秒）是同样重要的预测指标。因此，时间尺度在听众如何调整声学环境中的信号统计方面起着至关重要的作用。