Idemaru Kaori, Holt Lori L
Department of East Asian Languages and Literatures, University of Oregon.
Department of Psychology, Carnegie Mellon University.
J Exp Psychol Hum Percept Perform. 2014 Jun;40(3):1009-21. doi: 10.1037/a0035269. Epub 2013 Dec 23.
Speech perception flexibly adapts to short-term regularities of ambient speech input. Recent research demonstrates that the function of an acoustic dimension for speech categorization at a given time is relative to its relationship to the evolving distribution of dimensional regularity across time, and not simply to a fixed value along the dimension. Two experiments examine the nature of this dimension-based statistical learning in online word recognition, testing generalization of learning across phonetic categories. While engaged in a word recognition task guided by perceptually unambiguous voice-onset time (VOT) acoustics signaling stop voicing in either bilabial rhymes, beer and pier, or alveolar rhymes, deer and tear, listeners were exposed incidentally to an artificial "accent" deviating from English norms in its correlation of the pitch onset of the following vowel (F0) with VOT (Experiment 1). Exposure to the change in the correlation of F0 with VOT led listeners to down-weight reliance on F0 in voicing categorization, indicating dimension-based statistical learning. This learning was observed only for the "accented" contrast varying in its F0/VOT relationship during exposure; learning did not generalize to the other place of articulation. Another group of listeners experienced competing F0/VOT correlations across place of articulation such that the global correlation for voicing was stable, but locally correlations across voicing pairs were opposing (e.g., "accented" beer and pier, "canonical" deer and tear, Experiment 2). Listeners showed dimension-based learning only for the accented pair, not the canonical pair, indicating that they are able to track separate acoustic statistics across place of articulation, that is, for /b-p/ and /d-t/. This suggests that dimension-based learning does not operate obligatorily at the phonological level of stop voicing.
语音感知能够灵活地适应周围语音输入的短期规律。最近的研究表明，在给定时间用于语音分类的声学维度的功能，与其随时间变化的维度规律性分布的关系有关，而不仅仅取决于该维度上的固定值。两项实验研究了在线单词识别中这种基于维度的统计学习的本质，测试了跨语音类别的学习泛化情况。在由感知上明确的语音起始时间（VOT）声学引导的单词识别任务中，VOT在双唇韵“beer”和“pier”或齿龈韵“deer”和“tear”中表示塞音发声，听众偶然接触到一种人工“口音”，其后续元音的音高起始（F0）与VOT的相关性偏离了英语规范（实验1）。接触F0与VOT相关性的变化导致听众在发声分类中减少对F0的依赖，这表明了基于维度的统计学习。这种学习仅在暴露期间F0/VOT关系变化的“带口音”对比中观察到；学习并没有泛化到其他发音部位。另一组听众在不同发音部位经历了相互竞争的F0/VOT相关性，使得发声的全局相关性稳定，但发声对之间的局部相关性相反（例如，“带口音”的“beer”和“pier”，“标准”的“deer”和“tear”，实验2）。听众仅对带口音的对表现出基于维度的学习，而不是标准对，这表明他们能够跨发音部位跟踪单独的声学统计信息，即对于/b - p/和/d - t/。这表明基于维度的学习并非在塞音发声的语音层面上强制运作。
