From 8fb233cb5b17fe8052a2847238de7fc8586562ca Mon Sep 17 00:00:00 2001 From: Akshaya4225 Date: Sat, 28 Mar 2026 10:50:00 +0530 Subject: [PATCH] Update project_AutoEIT.md with clearer description Revised project description for clarity and added a 'Getting Started' section outlining initial steps for contributors. --- _gsocprojects/2026/project_AutoEIT.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/_gsocprojects/2026/project_AutoEIT.md b/_gsocprojects/2026/project_AutoEIT.md index 2b898713..b2415ecd 100644 --- a/_gsocprojects/2026/project_AutoEIT.md +++ b/_gsocprojects/2026/project_AutoEIT.md @@ -3,7 +3,27 @@ project: AutoEIT layout: default logo: AutoEIT.png description: | - AutoEIT is an applied machine learning project focused on automating the scoring of the Elicited Imitation Task (EIT), a research tool used to measure global language proficiency. The EIT is widely respected and is available for free in several languages, but the current workflow requires manual audio transcription and human scoring—slow, tedious, and error‑prone. This project aims to build an end‑to‑end system that will: Process raw audio files, perform accurate voice‑to‑text transcription, and automatically evaluate responses using a standardized scoring rubric. + AutoEIT is an applied machine learning project focused on automating the scoring of the Elicited Imitation Task (EIT), a research tool used to measure global language proficiency. + + The EIT is widely used and available in multiple languages. However, the current workflow relies on manual audio transcription and human scoring, which is slow, tedious, and prone to errors. + + This project aims to develop an end-to-end system with the following features: + + - Process raw audio files + - Perform accurate voice-to-text transcription + - Automatically evaluate responses + - Apply a standardized scoring rubric + +--- + +## Getting Started + +To begin contributing to AutoEIT: + +1. Understand the basics of speech-to-text processing +2. Explore audio datasets and preprocessing techniques +3. Learn machine learning models for transcription and evaluation +4. Review evaluation metrics for language proficiency scoring ---