617 Education Inc. (20240282213). SYSTEMS AND METHODS FOR GRAPHEME-PHONEME CORRESPONDENCE LEARNING simplified abstract

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SYSTEMS AND METHODS FOR GRAPHEME-PHONEME CORRESPONDENCE LEARNING

Organization Name

617 Education Inc.

Inventor(s)

Tom Dillon of Washington DC (US)

SYSTEMS AND METHODS FOR GRAPHEME-PHONEME CORRESPONDENCE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240282213 titled 'SYSTEMS AND METHODS FOR GRAPHEME-PHONEME CORRESPONDENCE LEARNING

The patent application describes systems and methods for learning grapheme-phoneme correspondence.

  • A device displays a grapheme graphical user interface (GUI) with a grapheme.
  • Audio data of a sound made by the user in response to the displayed grapheme is received.
  • A grapheme-phoneme model determines if the sound corresponds to the phoneme for the displayed grapheme.
  • The model is trained using augmented spectrogram data.
  • If the sound made by the user does not match the phoneme, a correct pronunciation is provided.
    • Key Features and Innovation:**
  • Utilizes a grapheme-phoneme model for learning pronunciation.
  • Trains the model using augmented spectrogram data.
  • Provides real-time feedback on correct pronunciation.
  • Enhances language learning through interactive user interface.
  • Improves accuracy in phoneme recognition.
    • Potential Applications:**
  • Language learning applications.
  • Educational tools for pronunciation practice.
  • Speech therapy and language rehabilitation programs.
  • Interactive language learning games.
  • Accessibility tools for individuals with speech impairments.
    • Problems Solved:**
  • Improving accuracy in pronunciation learning.
  • Enhancing interactive language learning experiences.
  • Providing real-time feedback on phoneme recognition.
  • Addressing challenges in grapheme-phoneme correspondence.
  • Supporting individuals with speech difficulties in learning pronunciation.
    • Benefits:**
  • Enhanced language learning outcomes.
  • Improved phoneme recognition accuracy.
  • Interactive and engaging learning experiences.
  • Personalized pronunciation feedback.
  • Accessibility for individuals with speech impairments.
    • Commercial Applications:**
  • Language learning software for schools and educational institutions.
  • Speech therapy applications for healthcare providers.
  • Interactive language learning apps for consumers.
  • Accessibility tools for individuals with speech impairments.
  • Potential integration into virtual reality language learning platforms.
    • Questions about Grapheme-Phoneme Correspondence:**

1. How does the grapheme-phoneme model determine correct pronunciation? 2. What are the potential implications of this technology for language education and speech therapy?


Original Abstract Submitted

systems and methods are described for grapheme-phoneme correspondence learning. in an example, a display of a device is caused to output a grapheme graphical user interface (gui) that includes a grapheme. audio data representative of a sound made by the human user is received based on the grapheme shown on the display. a grapheme-phoneme model can determine whether the sound made by the human corresponds to a phoneme for the displayed grapheme based on the audio data. the grapheme-phoneme model is trained based on augmented spectrogram data. a speaker is caused to output a sound representative of the phoneme for the grapheme to provide the human with a correct pronunciation of the grapheme in response to the grapheme-phoneme model determining that the sound made by the human does not correspond to the phoneme for the grapheme.

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