Paper Contents
Abstract
This project focuses on converting audio signals into text using Speech-to-Text (STT) APIs, which are categorized based on vocabulary size: small, medium, and large. Each system processes spoken language differently, transforming it into text. The paper presents a comparative analysis of these STT technologies, evaluating their advantages and limitations. Our experiments demonstrate the impact of language models on improving conversion accuracy, particularly with noisy sentences and incomplete words. The results indicate that randomly chosen sentences yield better performance compared to sequential ones. Additionally, the project aims to enhance communication for individuals with disabilities by incorporating graphical hand gestures, leveraging Natural Language Processing (NLP) principles to realize this goal.
Copyright
Copyright © 2024 Ms. Pokala Bharghavi. This is an open access article distributed under the Creative Commons Attribution License.
