Kleinschmidt Dave F
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Department of Brain and Cognitive Sciences, University of Rochester, New York, NY, USA.
Lang Cogn Neurosci. 2018;34(1):43-68. doi: 10.1080/23273798.2018.1500698. Epub 2018 Jul 30.
One of the persistent puzzles in understanding human speech perception is how listeners cope with talker variability. One thing that might help listeners is structure in talker variability: rather than varying randomly, talkers of the same gender, dialect, age, etc. tend to produce language in similar ways. Listeners are sensitive to this covariation between linguistic variation and socio-indexical variables. In this paper I present new techniques based on ideal observer models to quantify (1) the amount and type of structure in talker variation ( of a grouping variable), and (2) how useful such structure can be for robust speech recognition in the face of talker variability (the of a grouping variable). I demonstrate these techniques in two phonetic domains-word-initial stop voicing and vowel identity-and show that these domains have different amounts and types of talker variability, consistent with previous, impressionistic findings. An R package (phondisttools) accompanies this paper, and the source and data are available from osf.io/zv6e3.
理解人类言语感知过程中一个长期存在的谜题是听众如何应对说话者的变异性。可能有助于听众的一点是说话者变异性中的结构:相同性别、方言、年龄等的说话者往往不会随机变化,而是倾向于以相似的方式产生语言。听众对语言变异和社会索引变量之间的这种协变很敏感。在本文中,我提出了基于理想观察者模型的新技术,以量化:(1)说话者变异(分组变量的)结构的数量和类型,以及(2)面对说话者变异性时,这种结构对稳健语音识别有多大用处(分组变量的)。我在两个语音领域——词首塞音清浊和元音识别——中展示了这些技术,并表明这些领域具有不同数量和类型的说话者变异性,这与之前的印象主义研究结果一致。本文附带了一个R包(phondisttools),源代码和数据可从osf.io/zv6e3获取。