Speech impairment can significantly hinder an individual's ability to engage in verbal communication and listening. In communities where individuals are affected by this disability, sign language often becomes the primary mode of communication. However, non-sign language speakers may encounter challenges when attempting to communicate with those who are speech and hearing impaired. Leveraging modern advancements in deep learning and computer vision, particularly in the areas of motion and hand sign identification, presents an opportunity to overcome these challenges. The primary objective of this research is to develop a vision-based application that facilitates sign language transliteration into speech, fostering communication between verbal and non-verbal individuals. The proposed framework employs a systematic methodology involving the collection of video sequences, followed by the extraction of both temporal and spatial features. Spatial feature recognition is achieved through the utilization of “convolutional neural networks” (CNNs), while training on temporal features is conducted using “recurrent neural networks” (RNNs). The research utilizes the American Sign Language dataset as the foundation for training and testing the proposed framework. By combining deep learning techniques and computer vision, the system aims to accurately interpret and translate sign language gestures into spoken words. This approach not only bridges the communication gap between sign language speakers and non-sign language speakers but also empowers speech-impaired individuals to engage more seamlessly in conversations. The effectiveness of the proposed system is evaluated through comprehensive testing, considering factors such as accuracy, speed, and adaptability to different sign languages. The potential applications of this research extend beyond individual interactions to include educational and professional environments, where improved communication tools can enhance inclusivity and accessibility for speech-impaired individuals. This work contributes to the growing field of assistive technologies by leveraging cutting-edge advancements in deep learning and computer vision to create a practical solution for facilitating communication between individuals with speech impairments and those without knowledge of sign language.