用声音数据反推儿童腭咽闭合不全的发音动作，无创且更准。
Acoustic to Articulatory Speech Inversion for Children with Velopharyngeal Insufficiency
- 用声学+喉部信号做语音逆向建模，非侵入式估计发音动作。
- 在成人数据基础上微调后，对儿童患者预测相关性提升7.90%。
- 适合语音病理评估、儿童言语康复领域研究人员使用。
传统临床评估鼻音化的方法（如鼻咽内窥镜、鼻气流测量）对儿童来说体验不佳。语音逆向（Speech Inversion, SI）是一种无需物理仪器的非侵入技术，可估算发音动作。本研究在健康成人鼻音度数据训练的SI系统基础上，引入电声门图和声学提取的基频、周期性与非周期性能量作为声门控制的代理特征。该模型在鼻音度估计上相比先前系统实现16.92%的皮尔逊相关系数相对提升。为适配腭咽闭合不全（VPI）儿童的鼻音度估计，将原基于成人语音训练的模型，使用儿童VPI数据进行微调，使皮尔逊相关系数相较微调前提升7.90%。
原文摘要 · Abstract (English)
Traditional clinical approaches for assessing nasality, such as nasopharyngoscopy and nasometry, involve unpleasant experiences and are problematic for children. Speech Inversion (SI), a noninvasive technique, offers a promising alternative for estimating articulatory movement without the need for physical instrumentation. In this study, an SI system trained on nasalance data from healthy adults is augmented with source information from electroglottography and acoustically derived F0, periodic and aperiodic energy estimates as proxies for glottal control. This model achieves 16.92% relative improvement in Pearson Product-Moment Correlation (PPMC) compared to a previous SI system for nasalance estimation. To adapt the SI system for nasalance estimation in children with Velopharyngeal Insufficiency (VPI), the model initially trained on adult speech was fine-tuned using children with VPI data, yielding an 7.90% relative improvement in PPMC compared to its performance before fine-tuning.
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