Measuring sign complexity: Comparing a model-driven and an error-driven approach

IF 1.3 2区 文学 0 LANGUAGE & LINGUISTICS
Justine Mertz,Chiara Annucci,Valentina Aristodemo,Beatrice Giustolisi,Doriane Gras,Giuseppina Turco,Carlo Geraci,Caterina Donati
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引用次数: 0

Abstract

The study of articulatory complexity has proven to yield useful insights into the phonological mechanisms of spoken languages. In sign languages, this type of knowledge is scarcely documented. The current study compares an error-driven measure and a model-driven measure of complexity for signs in French Sign Language (LSF). The former measure is based on error rates of handshape, location, orientation, movement, and sign fluidity in a repetition task administered to non-signers; the latter measure is derived by applying a feature-geometry model of sign description to the same set of signs. A significant correlation is found between the two measures for the overall complexity. When looking at the effects of individual phonological classes on complexity, a significant correlation is found for handshape and location but not for movement. We discuss how these results indicate that a fine-grained theoretical model of sign phonology/phonetics reflects the degree of complexity as from the perceptual and articulatory properties of signs.
测量符号复杂性:比较模型驱动和错误驱动的方法
对发音复杂性的研究已被证明对口语的语音机制产生有用的见解。在手语中,这类知识几乎没有记录。目前的研究比较了法国手语(LSF)中符号复杂性的错误驱动度量和模型驱动度量。前一种测量是基于对非手语者进行重复任务时手部形状、位置、方向、运动和手势流动性的错误率;后一种度量是通过将符号描述的特征几何模型应用于同一组符号而得到的。在总体复杂性的两种度量之间发现了显著的相关性。当观察单个音系类别对复杂性的影响时,我们发现手的形状和位置有显著的相关性,但与动作没有相关性。我们讨论了这些结果如何表明,符号音韵学/语音学的细粒度理论模型反映了符号的感知和发音特性的复杂程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
6.70%
发文量
17
审稿时长
8 weeks
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