Subglottal air pressure plays a major role in voice production and is a primary factor in controlling voice onset, offset, sound pressure level, glottal airflow, vocal fold collision pressures, and variations in fundamental frequency. Previous work has shown promise for the estimation of subglottal pressure from an unobtrusive miniature accelerometer sensor attached to the anterior base of the neck during typical modal voice production across multiple pitch and vowel contexts. This study expands on that work to incorporate additional accelerometer-based measures of vocal function to compensate for non-modal phonation characteristics and achieve an improved estimation of subglottal pressure. Subjects with normal voices repeated /p/-vowel syllable strings from loud-to-soft levels in multiple vowel contexts (/a/, /i/, and /u/), pitch conditions (comfortable, lower than comfortable, higher than comfortable), and voice quality types (modal, breathy, strained, and rough). Subject-specific, stepwise regression models were constructed using root-mean-square (RMS) values of the accelerometer signal alone (baseline condition) and in combination with cepstral peak prominence, fundamental frequency, and glottal airflow measures derived using subglottal impedance-based inverse filtering. Five-fold cross-validation assessed the robustness of model performance using the root-mean-square error metric for each regression model. Each cross-validation fold exhibited up to a 25% decrease in prediction error when the model incorporated multi-dimensional aspects of the accelerometer signal compared with RMS-only models. Improved estimation of subglottal pressure for non-modal phonation was thus achievable, lending to future studies of subglottal pressure estimation in patients with voice disorders and in ambulatory voice recordings.

中文翻译：

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从产生非模态发声的健康扬声器的颈部表面振动中改进声门下压力估计

声门下气压在发声中起主要作用，是控制发声、偏移、声压级、声门气流、声带碰撞压力和基频变化的主要因素。先前的工作表明，在跨多个音高和元音上下文的典型模态语音产生期间，从连接到颈部前基部的不显眼的微型加速度计传感器估计声门下压力的前景。这项研究扩展了这项工作，以纳入额外的基于加速度计的声音功能测量，以补偿非模态发声特征并实现对声门下压力的改进估计。正常声音的受试者在多个元音上下文（/a/、/i/ 和 /u/）中从响亮到柔和的级别重复 /p/-元音音节字符串，音高条件（舒适、低于舒适、高于舒适）和语音质量类型（模态、呼吸、紧张和粗糙）。单独使用加速度计信号的均方根 (RMS) 值（基线条件）并结合使用基于声门下阻抗的逆推导出的倒谱峰隆起、基频和声门气流测量，构建了特定于对象的逐步回归模型过滤。五重交叉验证使用每个回归模型的均方根误差度量来评估模型性能的稳健性。与仅使用 RMS 的模型相比，当该模型包含加速度计信号的多维方面时，每个交叉验证折叠的预测误差降低了 25%。