通过CTW与EMA数据对齐评估音节产生的数学模型
Assessing a Mathematical Model of Syllable Production via CTW Alignment with EMA Data
浏览论文内容
中文总结 AI 辅助
本研究通过EMA数据与CTW对齐评估音节产生模型，发现模型输出与发音数据高度结构对应，验证了模型表征的有效性。
中文摘要 AI 辅助
本研究使用最近发表的一项研究中的EMA数据集评估了一个音节产生的数学模型，该数据集被证明与模型的架构高度兼容。该数据集由结构规则、时长相等且语音和音节复杂性降低的法语短语组成。采用专门程序将EMA记录转换为Maeda参数。随后，使用典型时间规整（CTW）将模型生成的轨迹与这些转换后的数据重新对齐。通过置换检验进行统计验证，将对齐质量与语音不协调的模型输出的替代条件进行比较。结果显示，语音协调配对的对齐显著更好，揭示了模型与发音数据之间的强结构对应关系。这些发现为模型表征的有效性提供了实验支持。
英文摘要
This study evaluates a mathematical model of syllable production using an EMA dataset from a recently published study, which proved highly compatible with the model's architecture. The data set consists of regularly structured French phrases of equal duration and reduced phonetic and syllabic complexity. A dedicated procedure was used to transform EMA recordings into Maeda parameters. The Model-generated trajectories were then realigned with these transformed data using Canonical Time Warping (CTW). Statistical validation was conducted via a permutation test, comparing alignment quality against a surrogate condition with phonetically incongruent model outputs. The results show significantly better alignment for phonetically congruent pairings, revealing a strong structural correspondence between the model and the articulatory data. These findings provide experimental support for the representational validity of the model.
发表机构
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab（格勒诺布尔阿尔卑斯大学，法国国家科学研究中心，格勒诺布尔理工学院，GIPSA实验室）
机构由 AI 辅助整理，请以论文原文为准。
