Artificial Intelligence Based Sign Language Prediction by Using the Twin Delayed Deep Reinforcement Memory Network architecture
Keywords:Sign language recognition, Artificial intelligence, Fibonacci ripple Chebyshev filter, Linear embedding Hessian component analysis, statistical looper wing butterfly optimization algorithm, Twin Delayed Deep reinforcement memory network
Communication between the hearing- and speech-impaired and the rest of society may be greatly aided by developments in Sign language recognition (SLR). One of the most important building blocks of sign language comprehension is word-level sign language recognition (WSLR). However, due to the fact that the meaning of a word relies on a wide range of subtle body gestures, hand configurations, and other behaviors, identifying signals in films may be difficult. Recent pose-based WSLR designs either represent the temporal information without completely utilizing the spatial information or explain the spatial but not the temporal correlations among the postures in various frames. To address the problem of WSLR, we use a novel approach based on AI to collect posture data and carry out recognition. To begin, we pulled the data and ran it through a Fibonacci ripple Chebyshev filter for preliminary cleaning (FRCF). Linear embedding Hessian component analysis (LEHCA) is then used to extract the features. The statistical looper wing butterfly optimization (SLWBO) approach is then used to segment the regions of interest. In the end, Twin Delayed Deep Reinforcement Memory Network (TDDRMN) architecture explicitly analyzes the feature interactions and recognizes the meaning of sign language to aid in the decision making process. The results derived on WLASL, a typical word-level sign language recognition dataset, show the system's superiority over the traditional approaches by obtaining high accurate prediction with minimum time in decision making.
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