SciELO - Scientific Electronic Library Online

 issue79A Markov random field image segmentation model for lizard spotsImpact of the error sensing probability in wide coverage areas of clustered-based wireless sensor networks author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google


Revista Facultad de Ingeniería Universidad de Antioquia

Print version ISSN 0120-6230


GOMEZ-GARCIA, Jorge Andrés; MORO-VELAZQUEZ, Laureano; GODINO-LLORENTE, Juan Ignacio  and  CASTELLANOS-DOMINGUEZ, César Germán. An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study. Antioquia [online]. 2016, n.79, pp.50-62. ISSN 0120-6230.

An automatic categorization of the speakers according to their sex improves the performance of an automatic detector of voice pathologies. This is grounded on findings demonstrating perceptual, acoustical and anatomical differences in males' and females' voices. In particular, this paper follows two objectives: 1) to design a system which automatically discriminates the sex of a speaker when using normophonic and pathological speech, 2) to study the influence that this sex detector has on the accuracy of a further voice pathology detector. The parameterization of the automatic sex detector relies on MFCC applied to speech; and MFCC applied to glottal waveforms plus parameters modeling the vocal tract. The glottal waveforms are extracted from speech via iterative lattice inverse filters. Regarding the pathology detector, a MFCC parameterization is applied to speech signals. Classification, in both sex and pathology detectors, is carried out using state of the art techniques based on universal background models. Experiments are performed in the Saarbrücken database, employing the sustained phonation of vowel /a/. Results indicate that the sex of the speaker may be discriminated automatically using normophonic and pathological speech, obtaining accuracy up to 95%. Moreover, including the a-priori information about the sex of the speaker produces an absolute performance improvement in EER of about 2% on pathology detection tasks.

Keywords : Voice pathology detection; inverse filtering; GMM; UBM.

        · abstract in Spanish     · text in English     · English ( pdf )