Emotion recognition is added a new dimension to the sentiment analysis. This paper presents a multi-modal human emotion recognition web application by considering of three traits includes speech, text, facial expressions, to extract and analyze emotions of people who are giving interviews. Now a days there is a rapid development of Machine Learning, Artificial Intelligence and deep learning, this emotion recognition is getting more attention from researchers. These machines are said to be intelligent only if they are able to do human recognition or sentiment analysis. Emotion recognition helps in spam call detection, blackmailing calls, customer services, lie detectors, audience engagement, suspicious behavior. In this paper focus on facial expression analysis is carried out by using deep learning approaches with speech signals and input text.
Sign language is a way of communication that helps people exchange information by using hand and arm gestures, commonly used by individuals who have difficulty hearing. However, sign language isn't universal, because impaired individuals from different countries use their corresponding sign languages. Using sign language, it allows us to communicate with impaired individuals including our loved ones, students in mainstream/deaf schools/colleges, locals and company owners, etc. Studies say learning sign language makes it simpler for a person to grasp lip-reading along with their native language. Most research has been done on Sign Language Translation/Recognition; different sign languages are translated into a common spoken language. However, the inverse is less, meaning limited research has been done on converting spoken languages to sign languages. Focusing on this matter, this study aims to translate speech/text into Indian Sign Language using the basics of Natural Language Processing.
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