Simulation Practice and Theory
Discriminating speakers with vocal nodules using aerodynamic and acoustic features
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Voice pathology detection by vocal cord biomechanical parameter estimation
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
New criteria for blind deconvolution of nonminimum phase systems (channels)
IEEE Transactions on Information Theory
Jitter estimation algorithms for detection of pathological voices
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Monitoring neurological disease in phonation
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Neurological disease detection and monitoring from voice production
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
KPCA vs. PCA study for an age classification of speakers
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Complexity analysis using nonuniform embedding techniques for voice pathological discrimination
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
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The Glottal Source is an important component of voice as it can be considered as the excitation signal to the voice apparatus. The use of the Glottal Source for pathology detection or the biometric characterization of the speaker are important objectives in the acoustic study of the voice nowadays. Through the present work a biometric signature based on the speaker's power spectral density of the Glottal Source is presented. It may be shown that this spectral density is related to the vocal fold cover biomechanics, and from literature it is well-known that certain speaker's features as gender, age or pathologic condition leave changes in it. The paper describes the methodology to estimate the biometric signature from the power spectral density of the mucosal wave correlate, which after normalization can be used in pathology detection experiments. Linear Discriminant Analysis is used to confront the detection capability of the parameters defined on this glottal signature among themselves and compared to classical perturbation parameters. A database of 100 normal and 100 pathologic subjects equally balanced in gender and age is used to derive the best parameter cocktails for pathology detection and quantification purposes to validate this methodology in voice evaluation tests. In a study case presented to illustrate the detection capability of the methodology exposed a control subset of 24+24 subjects is used to determine a subject's voice condition in a pre- and post-surgical evaluation. Possible applications of the study can be found in pathology detection and grading and in rehabilitation assessment after treatment.