Hello everyone, I am building a translator for my brother. My brother has been profoundly deaf since birth but he had speech therapy so he can speak a wide variety of words but not understandable to the regular person. But a random person can quickly grasp on what he means by which words in 2-3 months. For example: our close family can completely understand him well. So I wanted to make a translator app for him to navigate easily in real world. The purpose of the translator is to detect the atypical speech of my brother and translate it to typical speech. I talked with LLMs about this and they suggested finetuning a whisper model on a common phrases dataset. Since my brother speaks Bengali language, I made a common phrases dataset of around 500 with the help of AI. Now, I am taking his speech against those phrases and will later finetune a bengali whisper model. Since I am new to the field, I completely relied on AI to plan this whole thing. LLMs said that since an average person can understand him well in 2-3 months, model can learn it faster. I want to know am i on the right track or should i do anything else? I just wanna make sure I am not missing anything Thank you submitted by /u/Sadgeincomp
Originally posted by u/Sadgeincomp on r/ArtificialInteligence