Enhancing Automatic Speech Recognition With Personalized Models: Improving Accuracy Through Individualized Fine-Tuning

Automatic speech recognition (ASR) systems have become increasingly popular in recent years due to their ability to convert spoken language into text. Nonetheless, despite their widespread use, existing speaker-independent ASR systems frequently encounter challenges related to variations in speaking styles, accents, and vocal characteristics, leading to potential recognition inaccuracies. This study delves into the feasibility of personalized ASR systems that adapt to the unique voice attributes of individual speakers, thereby enhancing recognition accuracy. It provides an overview of our methodology, focusing on the design, development, and evaluation of both speaker-independent and personalized ASR systems. The dataset used included diverse speakers selected from three extensive datasets: TedLIUM-3, CommonVoice, and GoogleVoice, demonstrating the capability of our methodology to accommodate various accents and challenges of both natural and synthetic voices. In terms of signal classification and interpretation, the personalized model eclipsed the speaker-independent variant, registering an enhancement of up to similar to 3% for natural voices and similar to 10% for synthetic voices in recognition accuracy for individual speakers. Our findings demonstrate that personalized ASR systems can significantly improve the accuracy of speech recognition for individual speakers and highlight the importance of adapting ASR models to individual voices.