Sign and voice translation is a critical tool for individuals who cannot hear or speak , or for those who speak different languages. Machine learning techniques have been increasingly used to find or improve the accuracy and efficiency of sign and voice translation systems. These systems make use of machine learning models to analyze and interpret sign language or speech and translate them into written or spoken language. Machine learning models can recognize patterns in sign language gestures or speech, and convert them into text or speech output. The model`s accuracy is dependent on the quality of its training data and the complexity of the model architecture. Recent improvisation in machine learning has increased the performance of sign and voice translation systems, enabling them to recognize more complex gestures and accents. Overall, the use of machine learning in sign and voice translation has the potential to improve the accessibility of information and communication for individuals who are deaf or hard of hearing, or for those who speak different languages. However, there is still much room for improvement, and ongoing research and development are needed to optimize the performance of these systems

Computer Vision, Recognition of Sign Language, Hand Gesture Recognition, Features Extraction

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