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Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors

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2 Author(s)
Godino-Llorente, J.I. ; Dpt. of Ingenieria de Circuitos y Sistemas, Escuela Universitaria de Ingenieria Tecnica de Telecomunicacion, Valencia, Spain ; Gomez-Vilda, P.

It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders-including glottic cancer-under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

Published in:

Biomedical Engineering, IEEE Transactions on  (Volume:51 ,  Issue: 2 )

Date of Publication:

Feb. 2004

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