论文标题
大脑启发的概率生成模型用于口语的双重表达分析
Brain-inspired probabilistic generative model for double articulation analysis of spoken language
论文作者
论文摘要
人脑在其几种功能中，分析了口语中的双重表达结构，即双重关节分析（DAA）。单词连接到形成句子的层次结构，单词由音素或音节组成，称为双关节结构。尽管已经获得了一些见解，但尚未建立在人脑中DAA的何处以及如何进行DAA。此外，基于概率生成模型（PGM）的现有计算模型不包括神经科学的发现，并且以前尚未讨论过其与大脑的一致性。这项研究将这些现有计算模型与神经科学的发现进行了比较，映射和整合，以弥合这一差距，并且发现与未来的应用和进一步的研究有关。这项研究提出了一个DAA假设的PGM，该假设可以根据几种神经科学调查的结果在大脑中实现。该研究涉及（i）研究和组织与口语处理有关的解剖结构，以及（ii）与感兴趣区域的解剖结构和功能相匹配的PGM。因此，这项研究提供了新的见解，这些见解将是进一步探索大脑中DAA的基础。
The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in which words are connected to form a sentence and words are composed of phonemes or syllables is called a double articulation structure. Where and how DAA is performed in the human brain has not been established, although some insights have been obtained. In addition, existing computational models based on a probabilistic generative model (PGM) do not incorporate neuroscientific findings, and their consistency with the brain has not been previously discussed. This study compared, mapped, and integrated these existing computational models with neuroscientific findings to bridge this gap, and the findings are relevant for future applications and further research. This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys. The study involved (i) investigation and organization of anatomical structures related to spoken language processing, and (ii) design of a PGM that matches the anatomy and functions of the region of interest. Therefore, this study provides novel insights that will be foundational to further exploring DAA in the brain.
