A Voice Enabled Grammar Correction System using Transformers

This paper introduces a groundbreaking real-time grammar error detection and correction system (named “Error Ears”) that adapts to linguistic interactions. At the core of the proposed approach lies the utilization of the potent “T5” model, renowned for its capability to comprehend and produce human-like text. The proposed workflow meticulously covers essential stages including data pre-processing, model training, and fine-tuning. By fine-tuning the “T5” model on a diverse dataset of sentences with numerous grammatical errors, the goal is to empower it to distinguish complex contextual nuances in speech. Preliminary results indicate that “Error Ears” represents a substantial advancement over traditional correctional systems. This system excels at swiftly identifying and rectifying intricate spoken language errors, encompassing structural and contextual details. What sets it apart is its ability to provide real-time contextual suggestions, rendering it especially valuable during virtual meetings, presentations, and everyday conversations. “Error Ears” is poised to redefine spoken language communication standards by enhancing clarity and precision. Its potential utility extends across a spectrum of contexts and linguistic environments, ensuring more efficient and accurate oral communication. In the digital age, this study opens up new avenues for a better comprehension and expression of language, resulting in more effective and transparent communication.