基于 Seq2Seq LSTM 的改进声门波模型用于模拟声带振动。
An Improved Glottal Flow Model Based on Seq2Seq LSTM for Simulation of Vocal Fold Vibration.
机构信息
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 213300, China.
出版信息
J Voice. 2024 Sep;38(5):983-992. doi: 10.1016/j.jvoice.2022.03.029. Epub 2022 May 7.
OBJECTIVES
An improved data-driven glottal flow model for fluid-structure interaction (FSI) simulation of the vocal fold vibration is proposed in this paper. This model aims to improve the prediction performance of the previously developed deep neural network (DNN) based empirical flow model (EFM) on accuracy and efficiency.
METHODS
A Seq2Seq long short-term memory (LSTM) network is employed in the present model to infer the flow rate and pressure distribution from the subglottal pressure and cross-section area distribution of the glottis. The training data is collected from the generalized glottal shape library generated in Zhang et al. RESULTS AND CONCLUSIONS: Compared to the EFM, the present model not only discards the time-consuming optimization process, but also drastically reduces the errors, therefore the prediction performance can be greatly improved. The present model is evaluated by coupling with a solid dynamics solver for FSI simulation, and the results demonstrate a great improvement on accuracy and efficiency.
目的
本文提出了一种改进的数据驱动流固耦合（FSI）模拟声门振动的声门波模型。该模型旨在提高先前基于深度神经网络（DNN）的经验流模型（EFM）在准确性和效率方面的预测性能。
方法
本模型采用序列到序列长短期记忆（LSTM）网络，从声门下压力和声带截面积分布推断流量和压力分布。训练数据来自 Zhang 等人生成的广义声门形状库。
结果与结论
与 EFM 相比，本模型不仅省去了耗时的优化过程，而且大幅降低了误差，因此可以大大提高预测性能。本模型通过与固体动力学求解器耦合进行 FSI 模拟进行评估，结果表明在准确性和效率方面有了很大的提高。
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