Paper Info
Title
PRECISE RECONSTRUCTION OF THE MUCOSAL WAVE FOR VOICE PATHOLOGY DETECTION AND CHARACTERIZATION
Abstract
Voice recordings are the only basis for pathology detection and classification in critical cases where invasive instrumentation is not possible as in new-borns and long-distance screening, among others. Classical pathology detection methods using voice rely on processing basic information from the voice signal, as the pitch, jitter, shimmer, HNR and others similar. In the present work a new method to estimate HNR from the detection and processing of a signal correlate of the mucosal wave is presented, as it is well known that mucosal wave alterations give clues to the presence of certain pathologies. An evaluation of mucosal wave in recordings from normal and pathological cases is presented and discussed, checking the results against those produced from simulated voice by a 2-mass model. Through the present work the precise reconstruction of a mucosal wave correlate from voice using inverse filtering of real and simu- lated traces is presented. This signal is of most importance in es- tablishing the presence of certain pathologies in the vocal folds (8). In what follows the term mucosal wave will refer to the travel- ling wave effect taking place in the vocal cords due to the distribu- tion of masses on the body cover and related tissues, and the term mucosal wave correlate (MWC) will be used for the influence of mucosal wave on the overall pattern of the glottal aperture, ap- pearing as a superimposed ringing on its reconstructed trace. The MWC may be seen as a higher order vibration regime of the vocal folds, once the average main movement or first regime has been removed. To start the study a version of the vocal cord 2-mass model as given in (7) and (4) has been implemented in MATLAB® (5), its main features being: 2-mass asymmetric mod- elling, non-linear coupling between mass movement and glottal aperture, cord collision effects, non-linearities and deffective clo- sure effects taken into account, lung flux excitation and vocal tract coupling. The parameters of the model are the lumped masses (2 per cord) M1l and M2l (left cord), M1r and M2r (right cord), the elastic parameters K1l and K2l (relative to reference) and K12l (in- tercoupling), and their respective ones for the right cord: K1r, K2r and K12r. The dynamic equations of the model are a set of four integro-differential equations, one for each of the masses in the system, with the following structure:
Year
Venue
Field
2004
European Signal Processing Conference
Computer science,Speech recognition,Jitter,Pathology
DocType
ISBN
Citations 
Conference
978-320-0001-65-7
2
PageRank 
References 
Authors
0.62
3
7
Name
Order
Citations
PageRank
P. Gómez152.10
Fernando Díaz-de-María220132.14
R. Martínez32913.46
Juan Ignacio Godino-Llorente418230.35
A. Alvarez520.62
Luis Rodriguez6103.17
V. Rodellar7147.98