<p>Automated Speech Recognition (ASR) systems face significant challenges in accurately transcribing speech, especially in low-resource languages like Tamil, which have a variety of dialects and slang. In such languages, traditional ASR models struggle to adapt to these dynamic variations, often leading to frequent transcription errors when encountering new terms or informal speech patterns. To address these errors, existing systems commonly rely on predefined dictionaries. However, these dictionaries are static and cannot adapt to the different dialects and variations within the Tamil language which leads to inaccuracies. This limitation highlights the need for more flexible solutions which is capable of addressing the complexities of diverse and evolving speech patterns in Tamil Language. In this study, a novel auto-update mechanism called Pattern Matching with Levenshtein Distance for Dictionary-Based Text Correction (PMLD-DTC) is propose, which is integrated with the wav2vec2 ASR model. The PMLD-DTC technique enhances transcription by performing character-level matching with a dynamic vocabulary file, enabling real-time updates. This approach incorporates slang, regional variations, and new terms by auto-updating the dictionary which reduces manual intervention and improving adaptability to linguistic diversity of Tamil. Experimental validation was conducted on CodaLab dataset, and two Hugging face dataset, achieving an accuracy of 92% with an overall average Word Error Rate (WER) minimization of ~11.03%. Additionally, live transcription test was performed for the first time, where the system achieved 25% accuracy, which demonstrates the effectiveness of the proposed auto-update mechanism in real-time transcription scenarios.</p>