Supplementary Material for: Tongue and Lip Acceleration as a Measure of Speech Decline in Amyotrophic Lateral Sclerosis

Purpose: The goal of this study was to examine the efficacy of acceleration-based articulatory measures in characterizing the decline in speech motor control due to amyotrophic lateral sclerosis (ALS). Method: Electromagnetic articulography was used to record tongue and lip movements during the production of 20 phrases. Data were collected from 50 individuals diagnosed with ALS. Articulatory kinematic variability was measured using the spatiotemporal index (STI) of both instantaneous acceleration and speed signals. Linear regression models were used to analyze the relationship between variability measures and intelligible speaking rate (a clinical measure of disease progression). A machine learning algorithm (support vector regression, SVR) was used to assess whether acceleration or speed features (e.g., mean, median, maximum) showed better performance at predicting speech severity in patients with ALS. Results: As intelligible speaking rate declined, the variability of acceleration of tongue and lip movement patterns significantly increased (p<0.001). The variability of speed and vertical displacement did not significantly predict speech performance measures. Additionally, based on R2 and root mean square error (RMSE) values, the SVR model was able to predict speech severity more accurately from acceleration features (R2 = 0.601, RMSE = 38.453) and displacement features (R2 = 0.218, RMSE = 52.700) than from speed features (R2 = 0.554, RMSE = 40.772). Conclusion: Results from these models highlight differences in speech motor control in participants with ALS. The variability in acceleration of tongue and lip movements increases as speech performance declines, potentially reflecting physiological deviations due to the progression of ALS. Our findings suggest that acceleration is a more sensitive indicator of speech deterioration due to ALS than displacement and speed, and may contribute to improved algorithm designs for monitoring disease progression from speech signals.

研究目的：本研究旨在探究基于加速度的发音测量指标在表征肌萎缩侧索硬化症（amyotrophic lateral sclerosis, ALS）所致言语运动控制衰退中的有效性。
研究方法：本研究采用电磁发音描记法（electromagnetic articulography）记录20个短语发音过程中的舌部与唇部运动。共纳入50名确诊为ALS的受试者进行数据采集。分别基于瞬时加速度与速度信号的时空指数（spatiotemporal index, STI）计算发音运动学变异性。采用线性回归模型分析变异性指标与可理解言语速率（疾病进展的临床评估指标）之间的关联。此外，采用机器学习算法——支持向量回归（support vector regression, SVR）——评估加速度特征与速度特征（如均值、中位数、最大值）在预测ALS患者言语严重程度方面的性能优劣。
研究结果：随着可理解言语速率下降，舌部与唇部运动模式的加速度变异性显著升高（p<0.001）。而速度与垂直位移的变异性无法显著预测言语表现。此外，基于决定系数（R²）与均方根误差（root mean square error, RMSE）指标，相较于速度特征（R²=0.554，RMSE=40.772），SVR模型通过加速度特征（R²=0.601，RMSE=38.453）与位移特征（R²=0.218，RMSE=52.700）能够更精准地预测言语严重程度。
研究结论：上述模型结果揭示了ALS受试者言语运动控制的差异。随着言语表现衰退，舌部与唇部运动的加速度变异性升高，这可能反映了ALS进展所致的生理学异常。本研究结果表明，相较于位移与速度指标，加速度是表征ALS所致言语恶化的更敏感指标，或可助力基于言语信号监测疾病进展的算法优化。