A Natural Language Processing Approach to the Translation of Speech into Indian Sign Language

Communication is a critical aspect of every individual's interaction, and individuals typically exchange information in a variety of languages. However, individuals with hearing and speech impairments may encounter difficulties in communicating on par with the general population. Additionally, hearing-impaired individuals comprehend our thoughts through the use of sign language. Research indicates that acquiring sign language skills facilitates the comprehension of lip-reading as well as one's mother tongue. This is accurate for both literate and illiterate individuals throughout India. The approach involves converting speech into written text, which is going to go through text preprocessing utilizing natural language processing (NLP) techniques to enhance analysis. Text and voice input are accepted by the system, which compares these with the clips in the authors' database. It displays matching sign motions based on Indian Sign Language grammatical rules as a result if they match; if not, it proceeds through the lemmatization and tokenization stages. Natural language processing, which powers part-of-speech tagging, parsing, lemmatization, and tokenization, is the system's central component. An accuracy of 92% is achieved in the proposed strategy.