DEEP LEARNING-BASED HAND GESTURE RECOGNITION FOR SPEECH SYNTHESIS IN TELUGU

In a world increasingly reliant on technology, individuals had born with hearing impairments face significant communication challenges, leading to feelings of isolation and dependency. This paper addresses the pressing need to empower the deaf and mute community by proposing an innovative solution – Deep Learning-Based Hand Gesture Recognition for Speech Synthesis in Telugu. Deaf and mute individuals encounter barriers in expressing themselves verbally, hindering their integration into mainstream society. Conventional methods often fall short in providing effective communication channels, exacerbating the challenges faced by this community. The critical need for a comprehensive solution that facilitates seamless communication in their native language, Telugu is evident. Current sign language solutions lack precision, failing to capture nuanced gestures, limiting accuracy. Importantly, they overlook converting gestures into spoken Telugu, leaving a gap for the deaf and mute community. These systems also face real-time processing challenges, hindering natural communication in users native language. Our innovative approach utilizes advanced technology, specifically Deep Learning and a Convolutional Neural Network (CNN). This system significantly improves understanding of hand movements, achieving a remarkable 90% accuracy. When someone uses hand gestures, our system converts them into spoken Telugu, enhancing communication for deaf and mute individuals. This substantial improvement empowers native Telugu speakers by 80%, allowing them to express themselves more naturally and actively participate in daily life.