A probabilistic stochastic model for analysis on the epileptic syndrome using speech synthesis and state space representation

Abstract

A probabilistic stochastic model deals with the real life applications of networks such as wireless communication, signals, speech synthesis, biomedical data in terms of blood pressure, ECG, EEG and temperature of a human being etc. An important class of stochastic process is Markov process which possess the past forgetting property, that is the result arises from each incident rely on the present but not on the past. This Markov property enables reasoning and computation with the model that would be otherwise intractable. In this paper the speech disorder developed by the Febrile infection-related epilepsy syndrome (FIRES) disease whose symptoms are discussed using Markov chain modeling as a new technique and its properties using a pictorial representation to enable the identification of an effective speech disorder therapy.

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Correspondence to F. Anitha Florence Vinola.

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Anitha Florence Vinola, F., Padma, G. A probabilistic stochastic model for analysis on the epileptic syndrome using speech synthesis and state space representation. Int J Speech Technol 23, 355–360 (2020). https://doi.org/10.1007/s10772-020-09702-1

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Keywords

  • Probabilistic stochastic model
  • Irreducible Markov chain
  • Aperiodic Markov chain
  • Transition probability matrix
  • Recurrence time
  • Epileptic syndrome