Articulation skills of singing based on the biomechanical coordination of throat muscles
Abstract
With the development of vocal training in music art, singing skills have been continuously explored and innovated. At present, the articulation skills of singing mostly focus on the surface operations of pronunciation skills such as tongue position, mouth shape, and lip movements, lacking the consideration of throat muscles and their coordination in clear pronunciation. In addition, everyone’s pronunciation habits are different, resulting in the low clarity of pronunciation of traditional techniques. This paper used the biomechanical model to analyze the coordination of throat muscles and proposed a systematic and scientific singing pronunciation and articulation training method. The study first built a biomechanical model of throat muscles based on Mooney-Rivlin and CAD, and introduced FEA to perform dynamic simulation on the model to simulate the mechanical behavior of muscle groups under different vocalization states. Then, it took the pharyngeal bones and muscle groups as the research objects, constructed a multi-rigid body dynamics model, and established the dynamic relationship between muscle drive and skeletal movement. Finally, the paper designed personalized singing pronunciation and clarity techniques based on CI, muscle tension distribution, etc. The experiment took 60 healthy singers as subjects, set up an experimental group and multiple control groups, and explored the effectiveness of singing articulation clarity techniques from the perspective of throat muscle biomechanics and visual coordination. The experimental results showed that the pronunciation clarity of the experimental group reached 7 points and the STOI reached 0.84, while the traditional oral resonance training group only scored 5 points and the STOI reached 0.72. The experimental results show that the singing pronunciation clarity technique based on the biomechanical coordination of throat muscles can significantly improve the singing pronunciation clarity and enhance the live listening effect of the singing.
References
1. Marczyk A, Belley E, Savard C, Roy J P,Vaillancourt J,Tremblay P. Learning transfer from singing to speech: Insights from vowel analyses in aging amateur singers and non-singers[J]. Speech Communication, 2022, 141(1): 28-39.
2. Merrick G, Figol A, Anderson J, Lin R J. Outcomes of gender affirming voice training: A comparison of hybrid and individual training modules[J]. Journal of Speech, Language, and Hearing Research, 2022, 65(2): 501-507.
3. Chunyuan H. Research on the application efficacy of “pharyngeal voice training” in singing training[J]. Frontiers in Educational Research, 2022, 5(8):27-32.
4. Horyacheva A, Boyce K, Badesha M, Kerr C,Najeeb H,Namasivayam-MacDonald A. Identifying Non-Traditional Approaches to Swallowing Rehabilitation: A Scoping Review[J]. Dysphagia, 2024, 39(3): 321-347.
5. Korner A, Strack F. Articulation posture influences pitch during singing imagery[J]. Psychonomic Bulletin & Review, 2023, 30(6): 2187-2195.
6. Norris G. Vocal Traditions: Steiner Speech[J]. Voice and Speech Review, 2022, 16(2): 241-246.
7. Wang Y. The effectiveness of innovative technologies to manage vocal training: The knowledge of breathing physiology and conscious control in singing[J]. Education and Information Technologies, 2024, 29(6): 7303-7319.
8. Irianto I S, Gustyawan T, Handayani L. Implementation of Vocal Training Methods from the Stanislavski System in the Kanti Becakap[J]. Gondang: Jurnal Seni Dan Budaya, 2023, 7(1): 160-171.
9. Paparo S A. Singing with awareness: A phenomenology of singers’ experience with the Feldenkrais Method[J]. Research Studies in Music Education, 2022, 44(3): 541-553.
10. Zhu Q, Suanmonta T. The Singing Technique of Li Dongping in Guangxi’s Niu Ge Folk Opera: A Cultural and Historical Analysis[J]. Asia Pacific Journal of Religions and Cultures, 2024, 8(1): 229-241.
11. Tsuchimura K, Umemura N, Mori M. Effect of visual information on vowel intelligibility in Japanese singing[J]. Acoustical Science and Technology, 2023, 44(6): 446-449.
12. Jeanneteau M, Hanna N, Almeida A, Smith J,Wolfe J. Using visual feedback to tune the second vocal tract resonance for singing in the high soprano range[J]. Logopedics Phoniatrics Vocology, 2022, 47(1): 25-34.
13. Marin E, Unsihuay N, Abarca V E,Elias D A. Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech[J]. Sensors, 2024, 24(8): 2629.
14. Desjardins M, Apfelbach C, Rubino M, Abbott K V. Integrative review and framework of suggested mechanisms in primary muscle tension dysphonia[J]. Journal of Speech, Language, and Hearing Research, 2022, 65(5): 1867-1893.
15. Serry M A, Alzamendi G A, Zanartu M, Peterson S D. Modeling the influence of the extrinsic musculature on phonation[J]. Biomechanics and modeling in mechanobiology, 2023, 22(4): 1365-1378.
16. Zhang Z. Principal dimensions of voice production and their role in vocal expression[J]. The Journal of the Acoustical Society of America, 2024, 156(1): 278-283.
17. Marciniak-Firadza R. Oddychanie a głos i jego prawidłowa emisja[J]. Logopaedica Lodziensia, 2023 (7): 113-123.
18. Aydogus M, Ozkan E T. Immediate Effects of Vocal Warm-Up on the Acoustic and Aerodynamic Parameters of Speech-Language Pathology Students[J]. The Turkish Journal of Ear Nose and Throat, 2024, 34(3): 77-83.
19. Wrench A A. The compartmental tongue[J]. Journal of Speech, Language, and Hearing Research, 2024, 67(10): 3887-3913.
20. Kurbanova A, Aksoy S, Nalça Andrieu M, Oz U,Orhan K. Evaluation of the influence of hyoid bone position, volume, and types on pharyngeal airway volume and cephalometric measurements[J]. Oral Radiology, 2023, 39(4): 731-742.
21. He C, Chen L, Xiao T. Applicability of Mooney-Rivlin model for studying hyperelastic behavior of tongue[J]. Advances in Engineering Technology Research, 2024, 10(1): 73-73.
22. Enomoto S, Oda T. Effects of Region-Specific Material Properties of Patellar Tendon on the Magnitude and Distribution of Local Stress and Strain[J]. Advanced Biomedical Engineering, 2024, 13(1): 318-326.
23. Yulu Z, Jia L. Research progress on dynamic three-dimensional finite element model construction of temporomandibular joint[J]. International Journal of Frontiers in Medicine, 2023, 5(12):109-112.
24. Seo G, Park J H, Park H S, Roh J. Developing new intermuscular coordination patterns through an electromyographic signal-guided training in the upper extremity[J]. Journal of NeuroEngineering and Rehabilitation, 2023, 20(1): 112-128.
25. Ortega-Auriol P, Byblow W D, Besier T, McMorland A J C. Muscle synergies are associated with intermuscular coherence and cortico-synergy coherence in an isometric upper limb task[J]. Experimental Brain Research, 2023, 241(11): 2627-2643.
26. Liu J. Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production[J]. Molecular & Cellular Biomechanics, 2024, 21(1): 277-277.
27. Gong Y, Cheng Z, Teo E C, Gu Y. Finite Element Analysis of Cervical Spine Kinematic Response during Ejection Utilising a Hill-Type Dynamic Muscle Model[J]. Bioengineering, 2024, 11(7): 655-666.
28. Yang Q, Jin W, Zhang Q, Wei Y,Guo Z,Li X,et al. Mixed-modality speech recognition and interaction using a wearable artificial throat[J]. Nature Machine Intelligence, 2023, 5(2): 169-180.
29. Zhao F, Ji X, Shyy W, Xu K. High-order compact gas-kinetic schemes for three-dimensional flow simulations on tetrahedral mesh[J]. Advances in Aerodynamics, 2023, 5(1): 1-28.
30. Xu H Q, Gu S, Fan Y C, Li X S,Zhao Y F,Zhao J,et al. A strategy learning framework for particle swarm optimization algorithm[J]. Information Sciences, 2023, 619(1): 126-152.
31. Hosseinzadeh S, Goktürkler G, Turan-Karaoglan S. Inversion of self-potential data by a hybrid DE/PSO algorithm[J]. Acta Geodaetica et Geophysica, 2023, 58(2): 241-272.
32. Guo J, Wang J, Chen J, Ren G,Tian Q,Guo C. Multibody dynamics modeling of human mandibular musculoskeletal system and its applications in surgical planning[J]. Multibody System Dynamics, 2023, 57(3): 299-325.
33. Seifelnasr A, Si X, Ding P, Xi J. Liquid Dynamics in the Upper Respiratory–Digestive System with Contracting Pharynx Motions and Varying Epiglottis Angles[J]. Liquids, 2024, 4(2): 415-431.
34. Shariatzadeh M J, Hafshejani E H, Mitchell C J, Chiao M,Grecov D. Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal[J]. Biocybernetics and Biomedical Engineering, 2023, 43(2): 428-441.
35. Yogi R, Amhia H. Fourier and Wavelet Spectral Analysis on Elementary Time Domain Features in Emg Signals[J]. NeuroQuantology, 2022, 20(19): 4519-4525.
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