When a person has a genuine disability, they are unable to speak. The onset of this condition can make it difficult for a person to communicate with others, but there are many ways of interacting with others, including sign language, which is widely used. In sign language, a sequence of gestures can be used to represent the words of a phrase in a message. Sign language enables people to communicate with each other by using body language, such as gestures. This paper aims to find a translation of human sign language into speech that can be interpreted when it comes to interpreting gestures made by humans. To train the system, we use a deep convolutional neural network to build the dataset, hand gestures stored in a database and then analyze these hand gesture visuals with an appropriate model based on the hand gestures saved in the database. When an user accesses the application, it will automatically detect the gestures that have been saved in the database and display the corresponding results. It is possible to assist those who are deaf by using this system, while also making communication with them easier for everyone else.