[DMP 2024]: Create offline audio-phoetic matching model

[MohitNSamagra]

Ticket Contents

Description

The application is envisioned as an offline tool similar to Google's Read Along app but specifically for the Hindi language. It should present users with Hindi words and listen to the user's attempt to pronounce these words, providing feedback on the accuracy of their pronunciation.

Approaches for Consideration:

• Vector Representation of Words: Explore the possibility of maintaining vector representations of the required set of Hindi words. These vectors will be used to match against the vector-encoded recordings of spoken words by the user.
• Acoustic Word Encodings: Utilize acoustic word encodings to convert the list of Hindi words into a vector form. This encoding will then be used to match against the encoded recordings from users, determining the accuracy of pronunciation.
• Feedback Mechanism: Implement a feedback system that informs users of the correctness of their pronunciation and offers suggestions or corrections as needed.

Goals & Mid-Point Milestone

Goals

Setup/Installation

No response

Expected Outcome

No response

Acceptance Criteria

No response

Implementation Details

• Develop a robust and efficient algorithm for converting Hindi words and spoken recordings into vector representations that can be accurately compared.
• Ensure the app can run offline by storing all necessary data and models locally on the device.
• Design an intuitive user interface to encourage engagement and improve Hindi pronunciation skills.
• Pay attention to privacy and data security, especially concerning user recordings.

Mockups/Wireframes

No response

Product Name

Nipun Lakshya App

Organisation Name

SamagraX

Domain

⁠Education

Tech Skills Needed

Machine Learning, Natural Language Processing, Python

Mentor(s)

@GautamR-Samagra @CHA

Category

Machine Learning