Department of Psychological Sciences, and CT Institute for the Brain and Cognitive Sciences, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT, 06269-1020, USA.
Department of Psychology, University of Chicago, Chicago, IL, USA.
Atten Percept Psychophys. 2021 May;83(4):1842-1860. doi: 10.3758/s13414-020-02203-y. Epub 2021 Jan 4.
A fundamental problem in speech perception is how (or whether) listeners accommodate variability in the way talkers produce speech. One view of the way listeners cope with this variability is that talker differences are normalized - a mapping between talker-specific characteristics and phonetic categories is computed such that speech is recognized in the context of the talker's vocal characteristics. Consistent with this view, listeners process speech more slowly when the talker changes randomly than when the talker remains constant. An alternative view is that speech perception is based on talker-specific auditory exemplars in memory clustered around linguistic categories that allow talker-independent perception. Consistent with this view, listeners become more efficient at talker-specific phonetic processing after voice identification training. We asked whether phonetic efficiency would increase with talker familiarity by testing listeners with extremely familiar talkers (family members), newly familiar talkers (based on laboratory training), and unfamiliar talkers. We also asked whether familiarity would reduce the need for normalization. As predicted, phonetic efficiency (word recognition in noise) increased with familiarity (unfamiliar < trained-on < family). However, we observed a constant processing cost for talker changes even for pairs of family members. We discuss how normalization and exemplar theories might account for these results, and constraints the results impose on theoretical accounts of phonetic constancy.
言语感知中的一个基本问题是,听者如何(或是否)适应说话者言语产生方式的变化。关于听者如何应对这种可变性的一种观点认为,说话者差异被归一化——计算说话者特定特征和语音类别之间的映射,以便在说话者的声音特征的背景下识别语音。这种观点认为,当说话者随机变化时,听者处理语音的速度比当说话者保持不变时更慢。另一种观点认为,言语感知是基于记忆中围绕语言类别聚类的说话者特定听觉范例,允许独立于说话者的感知。这种观点认为,在进行语音识别训练后,听者在特定于说话者的语音处理方面会变得更加高效。我们通过测试具有非常熟悉的说话者(家庭成员)、新熟悉的说话者(基于实验室训练)和不熟悉的说话者的方法,来探究语音效率是否会随着说话者的熟悉度而增加。我们还询问了熟悉度是否会减少归一化的需求。正如预测的那样,语音效率(噪声中的单词识别)随着熟悉度的增加而增加(不熟悉<受过训练的<家人)。然而,即使是对于家庭成员对,我们也观察到说话者变化的恒定处理成本。我们讨论了归一化和范例理论如何解释这些结果,以及这些结果对语音恒常性理论解释的限制。