Efficient numerical simulation of the human voice

Maurerlehner, Paul; Schoder, Stefan; Freidhager, Clemens; Wurzinger, Andreas; Hauser, Alexander; Kraxberger, Florian; Falk, Sebastian; Kniesburges, Stefan; Echternach, Matthias; Döllinger, Michael

doi:10.1007/s00502-021-00886-1

Cited by 9 publications

(15 citation statements)

References 43 publications

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“… Buchaillard et al, 2009 ). 3) More knowledge is needed especially concerning the phonation process in order to be able to produce individual and naturally sounding voice qualities (e.g., Vasudevan et al, 2017 ; Maurerlehner et al, 2021 ). 4) More acoustic and aeroacoustic knowledge is needed for improving the quality of acoustic signal generation and signal modification within the vocal tract ( Zappi et al, 2016 ; Schoder et al, 2020 ).…”

Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

Section: Features For Differentiating Computer-implemented Articulato...mentioning

confidence: 99%

“…While in the first category of models the glottal flow can be calculated directly (e.g., Titze, 1989 ; McGowan and Howe, 2012 ), a complete mechanical model of the vocal folds needs to be implemented in case of the second category of models in order to calculate the vocal fold vibration pattern and the glottal area as function of time first before the glottal air flow can be calculated. Here the oscillatory motion pattern of the vocal folds results from the interactions of this mechanical system with the aerodynamic system (self-oscillating glottis models, e.g., Ishizaka and Flanagan, 1972 ; Story and Titze, 1995 ; Avanzini et al, 2006 ; Tao et al, 2006 ; Elie and Laprie, 2016 ; Maurerlehner et al, 2021 ). In case of both models aeroacoustic effects are important mainly for shaping the waveform of the glottal flow (e.g., McGowan and Howe, 2012 ) but aerodynamic and aeroacoustic effects can influence the pressure distribution along the glottal constriction as well and thus influence the aerodynamic forces acting on the vocal folds and thus influencing the glottal area oscillation pattern ( Pelorson et al, 1994 ; McPhail et al, 2019 ).…”

Section: Modeling Vocal Tract Aerodynamics and Acoustics: The Sound S...mentioning

confidence: 99%

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Computer-Implemented Articulatory Models for Speech Production: A Review

Kröger

2022

Front. Robot. AI

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Modeling speech production and speech articulation is still an evolving research topic. Some current core questions are: What is the underlying (neural) organization for controlling speech articulation? How to model speech articulators like lips and tongue and their movements in an efficient but also biologically realistic way? How to develop high-quality articulatory-acoustic models leading to high-quality articulatory speech synthesis? Thus, on the one hand computer-modeling will help us to unfold underlying biological as well as acoustic-articulatory concepts of speech production and on the other hand further modeling efforts will help us to reach the goal of high-quality articulatory-acoustic speech synthesis based on more detailed knowledge on vocal tract acoustics and speech articulation. Currently, articulatory models are not able to reach the quality level of corpus-based speech synthesis. Moreover, biomechanical and neuromuscular based approaches are complex and still not usable for sentence-level speech synthesis. This paper lists many computer-implemented articulatory models and provides criteria for dividing articulatory models in different categories. A recent major research question, i.e., how to control articulatory models in a neurobiologically adequate manner is discussed in detail. It can be concluded that there is a strong need to further developing articulatory-acoustic models in order to test quantitative neurobiologically based control concepts for speech articulation as well as to uncover the remaining details in human articulatory and acoustic signal generation. Furthermore, these efforts may help us to approach the goal of establishing high-quality articulatory-acoustic as well as neurobiologically grounded speech synthesis.

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Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

Section: Features For Differentiating Computer-implemented Articulato...mentioning

confidence: 99%

Section: Modeling Vocal Tract Aerodynamics and Acoustics: The Sound S...mentioning

confidence: 99%

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Computer-Implemented Articulatory Models for Speech Production: A Review

Kröger

2022

Front. Robot. AI

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“…To reduce the computational cost of a fluid-structure-acoustic interaction model [27], a prescribed vocal fold motion model is used [28]. This model consists of a finite-volume incompressible fluid dynamics simulation, and a finite-element acoustic simulation, as documented in [29]. In [24], the model has been used to model different regular and irregular phonation characteristics, such as various degrees of insufficient glottal closure, symmetric or asymmetric vocal fold motion, and different subglottal pressures.…”

Section: Methodsmentioning

confidence: 99%

“…The PCWE is solved using the finite-element solver openCFS [36]. An in-depth description of the CA simulations is available in [29,2]. Fig.…”

Section: Hybrid Aeroacoustic Simulation Setup For Human Phonation (Si...mentioning

confidence: 99%

Machine-learning applied to classify flow-induced sound parameters from simulated human voice

Kraxberger¹,

Wurzinger²,

Schoder³

2022

Preprint

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Disorders of voice production have severe effects on the quality of life of the affected individuals. A simulation approach is used to investigate the cause-effect chain in voice production showing typical characteristics of voice such as sub-glottal pressure and of functional voice disorders as glottal closure insufficiency and left-right asymmetry. Therewith, 24 different voice configurations are simulated in a parameter study using a previously published hybrid aeroacoustic simulation model. Based on these 24 simulation configurations, selected acoustic parameters (HNR, CPP, ...) at simulation evaluation points are correlated with these simulation configuration details to derive characteristic insight in the flow-induced sound generation of human phonation based on simulation results. Recently, several institutions studied experimental data, of flow and acoustic properties and correlated it with healthy and disordered voice signals. Upon this, the study is a next step towards a detailed dataset definition, the dataset is small, but the definition of relevant characteristics are precise based on the existing simulation methodology of simVoice. The small datasets are studied by correlation analysis, and a Support Vector Machine classifier with RBF kernel is used to classify the representations. With the use of Linear Discriminant Analysis the dimensions of the individual studies are visualized. This allows to draw correlations and determine the most important features evaluated from the acoustic signals in front of the mouth. The GC type can be best discriminated based on CPP and boxplot visualizations. Furthermore and using the LDA-dimensionality-reduced feature space, one can best classify subglottal pressure with 91.7% accuracy, independent of healthy or disordered voice simulation parameters.

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Overview on state-of-the-art numerical modeling of the phonation process

et al. 2023

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Numerical modeling of the human phonatory process has become more and more in focus during the last two decades. The increase in computational power and the use of high-performance computation (HPC) yielded more complex models being closer to the actual fluid-structure-acoustic interaction (FSAI) within the human phonatory process. However, several different simulation approaches with varying mathematical complexity and focus on certain parts of the phonatory process exist. Currently, models are suggested based on ordinary differential equations (reduced order models) but also on partial differential equations based on continuum mechanics as e.g. the Navier–Stokes equations for the flow discretized by Finite-Volume or Finite-Element-Methods. This review will illuminate current trends and recent progress within the area. In summary, the ultimate simulation model satisfying all physiological needs and scientific opinions still has to be developed.

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Efficient numerical simulation of the human voice

Cited by 9 publications

References 43 publications

Computer-Implemented Articulatory Models for Speech Production: A Review

Computer-Implemented Articulatory Models for Speech Production: A Review

Machine-learning applied to classify flow-induced sound parameters from simulated human voice

Overview on state-of-the-art numerical modeling of the phonation process

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