Doctoral Dissertation Research:Modeling temporal coordination in speech production using an artificial central pattern generator neural network

博士论文研究：使用人工中央模式生成器神经网络对语音产生中的时间协调进行建模

• 批准号: 1155592
• 负责人: Jennifer Cole
• 金额: $ 0.53万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1155592&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Modeling; temporal

This project investigates how timing is coordinated in speech production and how this coordination can be computationally modeled using artificial neural networks. Research has shown that speech rhythm reflects a multi-tiered, hierarchical organization of speech units (syllables, accentual units, phrases) that are essentially cyclical in nature. One type of neural network which shares these properties, the central pattern generator (CPG), has been hypothesized to underlie speech timing. This project presents two speech production experiments designed to investigate temporal coordination within and among three levels of speech units in French and English, languages with distinct rhythm types, and to discover which timing properties they share and which are language-specific. A three-level CPG-type artificial neural network is presented which is used to model the results of both experiments in order to test the ability of such a model to simulate the timing behavior of two rhythmically distinct languages. Experiment 1 focuses on the coordination between phrases, accentual units, and syllables through a comparison of the durational effects of lengthening due to phrasal stress and phrase-final syllable lengthening in both languages. Experiment 2 compares the overall temporal coordination of spoken phrases and their constituent parts in the two languages by measuring the changes in articulatory timing of phrases which are repeated multiple times. The main goals of the proposed studies and modeling work are to investigate temporal coordination between the hierarchically structured levels of speech units, specifically how that coordination varies between languages, and to test the ability of a biologically inspired CPG-type neural network model to model this coordination and the speech patterns that result from it.This work contributes to an understanding of the coordination and timing of rhythm units in speech and how that coordination may be generated by the brain. In its broader impact, it will enable the integration of theories of speech production with other motor behavior in biological and cognitive models, and help to forge links between research on speech, linguistic prosody, and more broadly, neural models of animal behavior. This work will also contribute to the construction of a unified model of rhythm and prosody which is sufficient to explain both the underlying universalities and the variations of human language. The project will also enrich the training of the graduate student co-PI.

该项目调查了语音产生中如何协调的时间，以及如何使用人工神经网络对这种协调进行计算模型。研究表明，语音节奏反映了本质上本质上是周期性的多层式语音单元（音节，精神单元，短语）的多层层次结构组织。共享这些属性的一种神经网络，即中央模式发生器（CPG），已被认为是语音时机的基础。该项目提出了两个语音生产实验，旨在调查法语和英语的三个级别语音单元内部和之间的时间协调，这些语言具有不同的节奏类型，并发现它们共享的定时属性和特定于语言的语言。提出了一个三级CPG型人工神经网络，用于对两个实验的结果进行建模，以测试这种模型模拟两种节奏不同语言的时序行为的能力。实验1的重点是通过比较两种语言中的短语应力和短语 - 最后一个音节延长延长的持续效应，介绍了短语，口音单元和音节之间的协调。实验2通过测量多次重复的短语的发音时机的变化，比较了两种语言中口语短语及其组成部分的总体时间协调。 The main goals of the proposed studies and modeling work are to investigate temporal coordination between the hierarchically structured levels of speech units, specifically how that coordination varies between languages, and to test the ability of a biologically inspired CPG-type neural network model to model this coordination and the speech patterns that result from it.This work contributes to an understanding of the coordination and timing of rhythm units in speech and how that coordination may由大脑产生。 在其更广泛的影响下，它将能够在生物学和认知模型中将语音生产理论与其他运动行为结合在一起，并有助于在言语，语言韵律和更广泛的动物行为神经模型之间建立联系。这项工作还将有助于建立一种统一的节奏模型和韵律模型，这足以解释潜在的普遍性和人类语言的变化。该项目还将丰富研究生Co-Pi的培训。