Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning

¹ Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India.
² Uttaranchal Institute of Management, Uttranchal University, Dehradun, India.
³ KG Reddy College of Engineering, Hyderabad, India.
⁴ Department of CSE, MLR Institute of Technology, Hyderabad, India.

^* Corresponding author: prasanna.yeluri@gmail.com

Abstract

Creating visuals from words may appear to be a complex process, but it is achievable with today’s technological advancements in Information Systems. Naturally, all the human-centric actions and assumptions may lead to visualization using Artificial Intelligence. In today’s Information Systems technological world, any item or a thing can be best described in pictorial form as a human person. Our paper aims to focus on providing machines with this intelligence. To complete this challenge, we used Natural Language Processing with Deep Learning. Our primary focus is on Generative Adversarial Networks. GANs will generate data based on word labels that are provided. NLP is also important since it helps to translate the provided speech into embedding vectors that the model can use. Our study is on the CUB dataset, which comprises bird photos. In today’s world, there are text-to-image generating models accessible. The authors investigated all of them, extending text-to-image generation to voice-to-image generation.