Voice production occurs through vocal cord and vibration coupled to glottal airflow. Vocal cord lesions affect the vocal system and lead to voice disorders. In this paper, a pathological voice source analysis system is designed. This study integrates nonlinear dynamics with an optimized asymmetric two-mass model to explore nonlinear characteristics of vocal cord vibration, and changes in acoustic parameters, such as fundamental frequency, caused by distinct subglottal pressure and varying degrees of vocal cord paralysis are analyzed. Various samples of sustained vowel /a/ of normal and pathological voices were extracted from MEEI (Massachusetts Eye and Ear Infirmary) database. A fitting procedure combining genetic particle swarm optimization and a quasi-Newton method was developed to optimize the biomechanical model parameters and match the targeted voice source. Experimental results validate the applicability of the proposed model to reproduce vocal cord vibration with high accuracy, and show that paralyzed vocal cord increases the model coupling stiffness.
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机译:声音的产生是通过声带和与声门气流耦合的振动产生的。声带损伤会影响声带并导致声音障碍。本文设计了一种病理性语音源分析系统。这项研究将非线性动力学与优化的非对称两质量模型相结合,以探索声带振动的非线性特征,并分析了声门下压力和声带麻痹程度不同所引起的声学参数(例如基本频率)的变化。从MEEI(马萨诸塞州眼与耳医院)数据库中提取了正常和病理性声音的持续元音/ a /的各种样本。结合遗传粒子群算法和拟牛顿法的拟合程序被开发出来,以优化生物力学模型参数并匹配目标声源。实验结果验证了所提模型在高精度再现声带振动中的适用性,并表明麻痹性声带增加了模型的耦合刚度。
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