International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 1999)
In this paper we consider a feature estimation approach for vocal fold pathology classification, based on digital signal processing theory. This problem is addressed by formulating a stochastic maximum likelihood (ML) estimation procedure, based on Estimation-Maximization (EM) algorithm. New spectral parameters of speech, noted as Spectral Pathology Component (SPC) is estimated. For classification purposes, the counterpropagation neural network (CNN) was proposed. A set of log Mel-frequency filter bank coefficients were used to parametrize the SPC spectral feature. An evaluation of CNN based classifier were performed using speech recording from healthy and pathology patients.
Bibliographic reference. Bovbel, Evgeny I. / Toumilovich, Mikhail A. (1999): "Stochastic approach to vocal fold pathology diagnostics", In MAVEBA-1999, 112-117.