Throat Polyps Detection Based on Patient Voices
In this paper, we present a new approach for throat polyps detection based on patient’s vowel voices using fuzzy classifiers. Based on human voice samples and Hidden Markov Model, we show that transformed voice samples (linearly combined samples) follow Gussian distribution, further we demonstrate that a type-2 fuzzy membership function (MF), i.e., a Gaussian MF with uncertain mean, is most appropriate to model the transformed voices samples. We also apply Short-Time-Fourier-Transform (STFT) and Singular-Value-Decomposition (SVD) to the vowel voice samples, and observe that the power decay rate could be used as an identifier in throat polyps detection. Two fuzzy classifiers and a Bayesian classifier are designed for throat polyps detection based on human vowel voices /a:/ and /i:/ only, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that an interval type-2 fuzzy classifier performs the best of the three classifiers.
KeywordsPolyps detection Fuzzy logic systems Bayesian classifier Interval type-2 fuzzy classifier Fuzzy membership functions
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