In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic barriers particularly evident in texting. This evolution facilitates global communication, transcending physical distances and fostering dynamic cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi and Russian transliterated into English utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models LLMs like BERT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99% our models robust performance underscores its reliability. The comprehensive exploration of transliteration dynamics supported by innovative approaches and cutting edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. Beyond contributing to language identification and transliteration capabilities this work holds promise for applications in content moderation, analytics and fostering a globally connected community engaged in meaningful dialogue.

翻译：在当代数字时代，互联网作为无与伦比的催化剂，打破了地理与语言障碍，尤其在短信交流中尤为显著。这一演变促进了全球沟通，跨越物理距离，推动动态文化交流。一个显著趋势是音译的广泛使用，即利用英文字母表达母语信息，这给语言技术在准确检测源语言方面带来了独特挑战。本文通过一个包含印地语和俄语电话短信音译成英文的数据集，利用BERT进行语言分类，并借助Google Translate API进行音译转换，以应对这一挑战。该研究开创性地探索了识别和转换音译文本的创新方法，应对数字通信中多元语言环境的挑战。强调全面数据集对训练大型语言模型（如BERT）的关键作用，本模型在从音译文本中准确识别和分类语言方面展现了卓越能力。验证准确率达99%，模型的稳健性能凸显其可靠性。通过创新方法与BERT等前沿技术支持的音译动态全面探索，本研究成果在应对数字通信语言环境独特挑战方面处于前沿。除了增强语言识别与音译能力外，这项工作在内容审核、分析以及促进全球互联社区进行有意义对话方面具有应用潜力。