Faculty of Life and Social Sciences, Swinburne University of Technology, Hawthorn, Australia.
Aix-Marseille Université and Centre National de la Recherche Scientifique, Marseille, France.
PLoS One. 2014 Apr 16;9(4):e94291. doi: 10.1371/journal.pone.0094291. eCollection 2014.
Most models of reading aloud have been constructed to explain data in relatively complex orthographies like English and French. Here, we created an Italian version of the Connectionist Dual Process Model of Reading Aloud (CDP++) to examine the extent to which the model could predict data in a language which has relatively simple orthography-phonology relationships but is relatively complex at a suprasegmental (word stress) level. We show that the model exhibits good quantitative performance and accounts for key phenomena observed in naming studies, including some apparently contradictory findings. These effects include stress regularity and stress consistency, both of which have been especially important in studies of word recognition and reading aloud in Italian. Overall, the results of the model compare favourably to an alternative connectionist model that can learn non-linear spelling-to-sound mappings. This suggests that CDP++ is currently the leading computational model of reading aloud in Italian, and that its simple linear learning mechanism adequately captures the statistical regularities of the spelling-to-sound mapping both at the segmental and supra-segmental levels.
大多数朗读 aloud 的模型都是为了解释相对复杂的 orthographies 中的数据而构建的，如英语和法语。在这里，我们创建了意大利语版的朗读 aloud 的连接主义双重过程模型（CDP++），以检验该模型在何种程度上可以预测具有相对简单的 orthography-phonology 关系但在超音段（单词重音）水平上相对复杂的语言的数据。我们表明，该模型表现出良好的定量性能，并解释了在命名研究中观察到的关键现象，包括一些明显矛盾的发现。这些效应包括重音规则性和重音一致性，它们在意大利语的单词识别和朗读 aloud 研究中尤为重要。总的来说，该模型的结果与能够学习非线性拼写-发音映射的替代连接主义模型相比表现良好。这表明，CDP++ 是目前意大利语朗读 aloud 的领先计算模型，其简单的线性学习机制充分捕捉了拼写-发音映射在音段和超音段水平上的统计规律性。
