• Prachi Lalage
• Shrikrushna Deore
• Shreyas Ghodchore
• Prachi Waghmare

Automated image captioning and voice generation have emerged as transformative technologies, enabling machines to interpret visual content and generate human-like descriptions. This study explores the integration of deep learning models, particularly Convolutional Neural Networks (CNNs) for image analysis and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for generating descriptive text. The research further investigates the role of text-to-speech (TTS) systems in converting these generated captions into natural-sounding speech. These technologies are crucial for improving accessibility, particularly for visually impaired individuals, and enhancing user engagement across multimedia platforms. The study highlights the impact of automated image captioning and voice generation in content creation, education, and accessibility. Challenges such as dataset availability, model accuracy, and computational complexity are discussed, with a focus on potential solutions and future research directions. Ultimately, the findings underscore the potential of these technologies to foster more inclusive, interactive, and engaging digital experiences.

Automated Image Captioning, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Text-to-Speech, Accessibility, Multimedia, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction.

"Automated Image Captioning And Voice Generation Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.b156-b163, March-2025, Available :http://www.jetir.org/papers/JETIR2503119.pdf

"Automated Image Captioning And Voice Generation Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppb156-b163, March-2025, Available at : http://www.jetir.org/papers/JETIR2503119.pdf