Assessment of vocal cord nodules: a case study in speech processing by using Hilbert-Huang Transform

12th International Conference on Damage Assessment of Structures

Civera, M.^1 ; Filosi, C.M.^2 ; Pugno, N.M.^3,4,5 ; Silvestrini, M.^2 ; Surace, C.^1 ; Worden, K.^6

Politecnico di Torino, Department of Structural, Building and Geotechnical Engineering, Corso Duca degli Abruzzi, 24, Turin

10129, Italy^1

Azienda Provinciale per i Servizi Sanitari-Provincia Autonoma di Trento, Trento

38123, Italy^2

Laboratory of Bio-Inspired and Graphene Nanomechanics, Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento

38123, Italy^3

School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London

E1 4NS, United Kingdom^4

Italian Space Agency, Via del Politecnico snc, Rome

00133, Italy^5

Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield

S1 3JD, United Kingdom^6

关键词: Adaptive noise;    Analysis and evaluation;    Empirical Mode Decomposition;    Hilbert Huang transforms;    Intrinsic Mode functions;    Pathological conditions;    Pathological voice;    Speech signals;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012025/pdf
DOI  :  10.1088/1742-6596/842/1/012025

来源: IOP

【 摘 要 】

Vocal cord nodules represent a pathological condition for which the growth of unnatural masses on vocal folds affects the patients. Among other effects, changes in the vocal cords' overall mass and stiffness alter their vibratory behaviour, thus changing the vocal emission generated by them. This causes dysphonia, i.e. abnormalities in the patients' voice, which can be analysed and inspected via audio signals. However, the evaluation of voice condition through speech processing is not a trivial task, as standard methods based on the Fourier Transform, fail to fit the non-stationary nature of vocal signals. In this study, four audio tracks, provided by a volunteer patient, whose vocal fold nodules have been surgically removed, were analysed using a relatively new technique: the Hilbert-Huang Transform (HHT) via Empirical Mode Decomposition (EMD); specifically, by using the CEEMDAN (Complete Ensemble EMD with Adaptive Noise) algorithm. This method has been applied here to speech signals, which were recorded before removal surgery and during convalescence, to investigate specific trends. Possibilities offered by the HHT are exposed, but also some limitations of decomposing the signals into so-called intrinsic mode functions (IMFs) are highlighted. The results of these preliminary studies are intended to be a basis for the development of new viable alternatives to the softwares currently used for the analysis and evaluation of pathological voice.