从学习的角度看抽象概念的产生：音素的奇特案例
A learning perspective on the emergence of abstractions: the curious case of phonemes
摘要：
在本论文中，我们使用了一系列的建模技术来研究是否可以出现一个抽象的电话从暴露于语音声音。实际上，这项研究代表了一种尝试，将基于使用的语言学的理论装置从语言使用中抽象出来。我们的探索集中在这些假设的抽象中最简单的。我们测试了两个对立的原则，关于语言知识的发展，在语言未经训练的语言使用者：基于记忆的学习（MBL）和纠错学习（ECL）。概括过程是语言学家所处理的抽象的基础，我们探讨了MBL和ECL是否可以产生一种类似于语言抽象的语言知识。每个模型都包含了由一个说话者产生的大量预处理语音。我们评估了这些简单模型所学到的东西的一致性或稳定性，以及它们产生抽象类别的能力。这两种类型的模型在这些测试中表现不同。我们表明，ECL模型可以学习抽象和至少部分的电话库存和分组到传统类型可以可靠地确定从输入。
英文摘要：
In the present paper we use a range of modeling techniques to investigate whether an abstract phone could emerge from exposure to speech sounds. In effect, the study represents an attempt for operationalize a theoretical device of Usage-based Linguistics of emergence of an abstraction from language use. Our quest focuses on the simplest of such hypothesized abstractions. We test two opposing principles regarding the development of language knowledge in linguistically untrained language users: Memory-Based Learning (MBL) and Error-Correction Learning (ECL). A process of generalization underlies the abstractions linguists operate with, and we probed whether MBL and ECL could give rise to a type of language knowledge that resembles linguistic abstractions. Each model was presented with a significant amount of pre-processed speech produced by one speaker. We assessed the consistency or stability of what these simple models have learned and their ability to give rise to abstract categories. Both types of models fare differently with regard to these tests. We show that ECL models can learn abstractions and that at least part of the phone inventory and grouping into traditional types can be reliably identified from the input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
