Analysis of Model Progression and Parity in Personalized Modeling of Lipsync Decoder to Cater Hearing Impaired Individuals
Akshay A 1, Sai Varun T2, Yesvanthraja D3, Nisha Devi K4
1Student, Department of Artificial Intelligence and Data Science,
Bannari Amman Institute of Technology, Sathyamangalam,
2Student, Department of Artificial Intelligence and Data Science,
Bannari Amman Institute of Technology, Sathyamangalam,
3Student, Department of Artificial Intelligence and Data Science,
Bannari Amman Institute of Technology, Sathyamangalam,
4Assistant Professor, Department of Artificial Intelligence and Data Science,
Bannari Amman Institute of Technology, Sathyamangalam.
Abstract – The Analysis of model progression and parity in personalized modeling of Lipsync decoder to cater hearing impaired individuals aims to enhance communication accessibility for hearing-impaired individuals by understanding flaws in and helping bring light to them translating visual cues from lip movements into intelligible speech through advancements in machine learning and computer vision. This paper delves into the analysis of model progression and parity in personalized modeling of Lipsync decoder technology to enhance communication accessibility for hearing-impaired individuals. By implementing the LipNet model through meticulous data collection, customization, and rigorous testing, the study aims to address challenges in real-time lip sync probe decoding. Results reveal a variation in word error rate when tested upon different video formats, highlighting the consistency, resilience, and potential for personalized modeling to improve communication accessibility for individuals with hearing impairments. The research underscores the critical importance of accurate lip reading and real-time processing in achieving seamless translation of lip movements into understandable speech, emphasizing the necessity for precision and efficiency in communication technologies for the hearing-impaired community.
Keywords: LipSyncProbe Decoder, Hearing-impaired individuals, real-time feed, LipNet model, datasets.
