Authors:
Rubén Fraile
1
;
Nicolás Sáenz-Lechón
1
;
Juan I. Godino-Llorente
2
;
Víctor Osma-Ruiz
1
and
Corinne Fredouille
3
Affiliations:
1
Universidad Politecnica de Madrid, Spain
;
2
Dept. Ingeniería de Circuitos y Sistemas, Universidad Politécnica de Madrid, Spain
;
3
Universite d'Avignon, France
Keyword(s):
Speech analysis, Pattern classification, Biomedical signal analysis, Communication channels.
Related
Ontology
Subjects/Areas/Topics:
Acoustic Signal Processing
;
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
Advances in speech signal analysis during the last decade have allowed the development of automatic algorithms for a non-invasive detection fo laryngeal pathologies. Performance assessment of such techniques reveals that classification success rates over 90% are achievable. Bearing in mind the extension of these automatic methods to remote diagnosis scenarios, this paper analyses the performance of a pathology detector based on Mel Frequency Cepstral Coefficients when the speech signal has undergone the distortion of an analogue communications channel, namely the phone channel. Such channel is modeled as a concatenation of linear effects. It is shown that while the overall performance of the system is degraded, success rates in the range of 80% can still be achieved. This study also shows that the performance degradation is mainly due to band limitation and noise addition.