{"title":"Improvement and discussion on pronunciation method of DIVA model based on auditory perception space","authors":"Shaojing Zhang, Xin-Bing Xu","doi":"10.1109/DIPDMWC.2016.7529354","DOIUrl":null,"url":null,"abstract":"Based on DIVA(Directions Into of Articulators) model, the paper attempts to construct a neural network model which can better simulate speech perception, speech acquisition and speech production. The model classifies speech phonemes from formant frequencies by unsupervised learning, and through the modifications of DIVA model speech mapping and self-organizing mapping algorithm, it simulates the natural process of human speech acquisition in auditory. What's more, by the comparison with achievements of psychological phonetics experiments, the simulation result shows that the model we build can better identify the difference between prototype and non-prototype and fully form auditory perception for vowels, so it better shows the natural process of the ability of human speech acquisition.","PeriodicalId":298218,"journal":{"name":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DIPDMWC.2016.7529354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Based on DIVA(Directions Into of Articulators) model, the paper attempts to construct a neural network model which can better simulate speech perception, speech acquisition and speech production. The model classifies speech phonemes from formant frequencies by unsupervised learning, and through the modifications of DIVA model speech mapping and self-organizing mapping algorithm, it simulates the natural process of human speech acquisition in auditory. What's more, by the comparison with achievements of psychological phonetics experiments, the simulation result shows that the model we build can better identify the difference between prototype and non-prototype and fully form auditory perception for vowels, so it better shows the natural process of the ability of human speech acquisition.
本文试图在DIVA(Directions Into of Articulators)模型的基础上,构建一个能够更好地模拟语音感知、语音获取和语音产生的神经网络模型。该模型采用无监督学习的方法对语音音素进行共振频率分类,并通过对DIVA模型语音映射和自组织映射算法的改进,模拟了人类在听觉环境下语音习得的自然过程。此外,通过与心理语音学实验成果的对比,仿真结果表明,我们构建的模型能够更好地识别原型与非原型的区别,并对元音形成完整的听觉感知,从而更好地体现了人类语言习得能力的自然过程。