Word meaning co-determines vowel-inherent spectral change. A corpus-based investigation of conversational Mandarin

📅 2026-07-23
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🤖 AI Summary
This study investigates whether lexical semantics influences vowel-intrinsic spectral change (VISC) in spontaneous Mandarin Chinese speech. By integrating generalized additive models with contextualized word embeddings, the research examines the predictive power of semantic information on vowel F1/F2 trajectories while controlling for multiple acoustic and linguistic variables. The results demonstrate that contextualized word embeddings significantly predict dynamic vowel formant trajectories, with prediction accuracy substantially exceeding that of permutation-based baselines. This finding provides the first empirical evidence of a dynamic coupling between semantic content and fine-grained articulatory detail in speech production, challenging traditional modular models of speech generation and offering new insights into the interface between language and phonetic implementation.
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

vowel-inherent spectral change
word meaning
speech production
formant trajectory
distributional semantics
Innovation

Methods, ideas, or system contributions that make the work stand out.

vowel-inherent spectral change
contextualized word embeddings
speech production
distributional semantics
generalized additive model