Dynamic Hand Gesture Recognition and Real-Time Utterance Generation for Virtual Assistants Using Machine Learning

A novel virtual assistant machine learning approach based on the study is known as dynamic hand gesture recognition using real-time vocal generation. This single method solution for the same uses a deep learning model CNN, which actually recognizes the real-time hands and maps that to recognize the real-time vocal generator with the training of models that produce responses that are pertinent to context by using this gesture data. This system attempts to allow for the creation of logical, spoken responses through natural, instinctive communications by decoding gestures such as volume control requests, play, and stop. Accuracy was improved in gesture recognition as well as the latency of generating a response in order to satisfy the requirement of real-time interaction with a system latency of 100 ms and a high rate of 94% for the identification of gestures. In a nutshell, it has been proved that the method proposed reaches the goal of communication simplification and diversification for virtual assistants and it also shows how gesture-based interaction might replace text or voice commands in specific contexts.