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