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Throat Polyps Detection Based on Patient Voices

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Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 202))

Abstract

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.

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References

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Correspondence to Zhen Zhong .

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© 2012 Springer Science+Business Media New York

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Zhong, Z., Chen, Z., Liang, Q., Xiao, S. (2012). Throat Polyps Detection Based on Patient Voices. In: Liang, Q., et al. Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 202. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5803-6_54

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  • DOI: https://doi.org/10.1007/978-1-4614-5803-6_54

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-5802-9

  • Online ISBN: 978-1-4614-5803-6

  • eBook Packages: EngineeringEngineering (R0)

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