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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Standard Entropy Change for a Reaction03:00

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Entropy and Solvation02:05

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Entropy within the Cell01:22

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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Entropy-based Dyslalia Screening.

Emilian-Erman Mahmut1, Michele Della Ventura2, Dorin Berian1

  • 1Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania.

Studies in Health Technology and Informatics
|July 27, 2019
PubMed
Summary

This study introduces a Computer-based Speech Therapy System (CBST) for screening Speech Sound Disorders (SSD), specifically dyslalia. An Entropy-based method shows promise for early detection and intervention in speech therapy.

Keywords:
dyslalia screeningentropyspeech therapy

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Area of Science:

  • Speech-Language Pathology
  • Computer Science
  • Biomedical Engineering

Background:

  • Speech Sound Disorders (SSD) present a significant challenge in early childhood development.
  • Existing Computer-based Speech Therapy Systems (CBST) offer potential but require further refinement for specific disorder screening.
  • Dyslalia, a common SSD, necessitates accurate and accessible screening methods.

Purpose of the Study:

  • To propose and evaluate a novel Computer-based Speech Therapy System (CBST) for the effective screening of dyslalia.
  • To address the limitations of current general CBST solutions in identifying specific speech sound disorders.
  • To present the development and experimental validation of an Entropy-based method for dyslalia screening.

Main Methods:

  • Development of a Computer-based Speech Therapy System (CBST) incorporating an Entropy-based algorithm.
  • Experimental validation of the proposed Entropy-based method for dyslalia screening.
  • Analysis of system performance and identification of areas for improvement.

Main Results:

  • The proposed Entropy-based method demonstrates potential for accurate dyslalia screening within the CBST framework.
  • Initial experimental results indicate the feasibility and effectiveness of the developed system.
  • Identified limitations provide a clear roadmap for future enhancements.

Conclusions:

  • The developed CBST with an Entropy-based method offers a promising approach for early dyslalia detection.
  • Further research and development are recommended to optimize the system's accuracy and scope.
  • The findings contribute to advancing digital health solutions in speech-language pathology.