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Voice-Enabled Local Language Translator Using Generative AI

 

 

1st Dr. V. Shanmugapriya

Department of Computer Science

(of Affiliation)

Sri Krishna Arts and Science College

(of Affiliation)

Coimbatore, India

shanmugapriyav@skasc.ac.in

 

 

2nd J N Pravanthika

Department of Computer Science

(of Affiliation)

Sri Krishna Arts and Science College

(of Affiliation)

Coimbatore, India

pravanthikajn24bcs036@skasc.ac.in

 

 

Abstract- Globalization and digital transformation have increased the need for real-time translation that bridges diverse linguistic communities. Traditional text-based tools are giving way to voice-enabled translators, which offer more natural and accessible communication, especially for low-resource languages. This paper presents a voice-enabled local language translator powered by Generative AI, integrating Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), generative translation, and Text-to-Speech (TTS) synthesis for accurate, context-aware output. The study reviews the evolution from rule-based to generative approaches, highlights challenges such as data scarcity, cultural sensitivity, latency, and ethics, and explores applications in education, healthcare, governance, commerce, and tourism. Future directions include multimodal AI, federated learning, next-generation networks, and preservation of endangered languages.

Keywords: Voice-enabled translation, Generative AI, ASR, NLU, TTS, Neural Machine Translation, Low-resource languages, Real-time multilingual communication, Edge AI, Endangered language preservation.

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