Doctoral Dissertation Research: Energy measures in the stop VOICING contrast

本论文的研究旨在更好地理解听觉是如何通过移动我们的发声器官的行为以及跨语言发音差异的声学和感知含义。我们知道有许多因素有助于我们利用声信号的能力，如发音强度、交易关系和对比声能量。这里讨论的基本理论问题是，发音本身是否对抽象手势作为独特特征有直接影响。该项目建立在早期研究能量特性作用的基础上。声学数据将收集英语、荷兰语、德语和法语等不同类型语言的一系列母语人士。舌位和气压数据在一个音节的开始和结束爆破音将与录音同步。这些语言的综合数据集将用于，首先，建立感知和感知研究将进行的边界条件，其次，增加我们对语音链上发音-声学关系的理解。较低谐波的相对频谱能量水平将被测量和评估，以及当说话者在这些辅音之后或之前发出元音时它们的变化特征。与传统的发声时间、元音长度、频率变化等指标相比，这些频谱能量变化特征在不同语境和不同语言类型下与发声对比的相关性更大。从声学和发音数据收集中获得的信息将用于生成标记，以便在随后的一组感知实验中进行测试。这项工作对自动语音识别技术具有启示意义。

This dissertation research seeks to better understand how listening is informed by the act of moving our vocal apparatus as well as the acoustic and perceptual implications of crosslinguistic articulatory differences. A number of factors are known to contribute to our ability to make use of the acoustic signal such as articulatory strength, trading relations, and acoustic energy in contrasts. The fundamental theoretical question addressed here is whether articulation per se has direct implications for abstract gestures as distinctive features. The project builds on earlier work investigating the role of energy characteristics. Acoustic data will be collected for a range of native speakers of such typologically distinct languages as English, Dutch, German and French. Tongue position and air pressure data for plosive sounds at the beginning and end of a syllable will be synchronized with the acoustic recordings. A comprehensive data set for these languages will be used to, first, establish percepts and the boundary conditions within which a perception study will be conducted, and, second, increase our understanding of the articulation-acoustic relation across the speech chain. The relative spectral energy levels in lower harmonics will be measured and evaluated, along with their changing characteristics as a speaker makes a vowel after or before one of these consonants. These spectral energy change characteristics have been found to be more correlated with voicing contrasts than the traditional measures of voice onset time, vowel length, frequency changes, etc. across contexts and across language types. Information gained from the acoustic and articulatory data collection will be used to generate tokens to be tested in a subsequent set of perception experiments. The work has implications for automatic speech recognition technology.