发表机构
Eberhard Karls Universität Tübingen; Center for Language Studies, Radboud University（图宾根大学; 拉德堡德大学语言研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究汉语会话中元音固有频谱变化，用广义相加模型和词嵌入，发现控制多变量时元音共振峰轨迹有与词义相关特定成分，可由语境化嵌入预测，挑战了语音产生模块化认知模型，揭示词义对发音细节的影响。
AI 中文摘要
本研究调查了汉语自然会话中的元音固有频谱变化（VISC）。使用广义相加模型和分布语义学中的词嵌入，研究表明，在控制元音时长、性别、说话者身份、协同发音、元音身份和话语位置等变量时，元音共振峰轨迹动态具有与语境意义相关的特定词成分。单词的F1和F2轨迹可从其语境化嵌入中预测，准确率大幅超过排列基线。这些结果挑战了语音产生的模块化认知模型，表明词义共同决定发音细节。
英文摘要
This study investigates vowel-inherent spectral change (VISC) in spontaneous conversational Mandarin. Using the generalized additive model and word embeddings from distributional semantics, we show that, when controlling for variables such as vowel duration, gender, speaker identity, co-articulation, vowel identity, and utterance position, vowel formant trajectory dynamics have word-specific components that are tied to their meaning in context: The F1 and F2 trajectories of words can be predicted from their contextualized embeddings with an accuracy that substantially exceeds a permutation baseline. Challenging modular cognitive models of speech production, these results indicate that, words' semantics co-determine the fine details of their articulation.
