International Research Journal of Education and Technology

Voice cloning technology has made significant strides in recent years, particularly in the realm of natural language processing (NLP) and speech synthesis. This paper explores the application of advanced techniques such as Tacotron2 and WaveNet in the context of Indian regional language voice cloning. The integration of Tacotron2 and WaveNet offers a promising avenue for bridging this gap and empowering Indian communities with voice technologies that reflect their linguistic heritage. By leveraging transfer learning and data augmentation methods, researchers have adapted these models to a diverse array of Indian languages and dialects, ranging from widely spoken languages like Hindi and Bengali to lesser-known languages with limited textual resources. The results obtained through this integration have been groundbreaking, with synthesized voices exhibiting remarkable fluency, intonation, and accent fidelity. This level of accuracy is crucial for ensuring that the synthesized voices sound natural and authentic to native speakers of Indian regional languages. This paper discusses the implications of voice cloning technology for Indian regional languages, including its potential to empower communities, foster inclusivity, and stimulate innovation. Overall, voice cloning for Indian regional languages holds immense promise in democratizing access to technology, preserving linguistic heritage, and promoting cultural diversity in one of the world's most linguistically diverse nations. Continued research and development efforts in this area are essential to realizing the full potential of voice cloning technology for Indian languages and ensuring equitable access to the benefits of AI-driven speech synthesis. Keywords— Voice cloning, Natural language processing, WaveNet, Transfer learning, Indian regional languages