Speech-to-Sign Language Translator Using NLP

Communication plays a vital role in people’s life and is regarded an important skill in life. A large number of people with speech and hearing impairment in our country use Indian Sign Language (ISL) as their primary mode of communication. Sign language is a non-verbal communication system in which people communicate by only using their visual sign patterns to express their meaning. Sign language serves as the primary mode of communication for individuals with speaking and/or hearing disabilities. However, due to limited proficiency in sign language among a substantial portion of the population, the Speech to Sign Language Translator emerges as a potential solution for effective communication among those unfamiliar with sign language. This translator employs machine learning techniques and a trained dataset to convert text and speech input in English into expressive actions and gestures of the standard Indian sign language, as performed by an animated avatar on the webpage [1]. The audio-to-sign language translator utilizes natural language processing techniques implemented in Python, employing machine learning algorithms for model training, and leveraging full-stack development technologies to construct the web page interface and embed the trained model. This tool offers convenience and real-world interpretability, enabling more efficient communication with individuals lacking sign language fluency. Future advancements can enhance this technology to support multiple languages worldwide, enabling the translation of text or speech into their respective sign languages. Consequently, the Sign Language Translator functions as a communication tool and assumes the role of a comprehensive 'bilingual dictionary webpage' for individuals with speaking or hearing disabilities.

Keywords: Lemmatizing, Natural language processing, Sign language translator, Speech-to-sign language.