Using Viseme Recognition to Improve a Sign Language Translation System

Sign language-to-text translation systems are similar to spoken language translation systems in that they consist of a recognition phase and a translation phase. First, the video of a person signing is transformed into a transcription of the signs, which is then translated into the text of a spoken language. One distinctive feature of sign languages is their multi-modal nature, as they can express meaning simultaneously via hand movements, body posture and facial expressions. In some sign languages, certain signs are accompanied by mouthings, i.e. the person silently pronounces the word while signing. In this work, we closely integrate a recognition and translation framework by adding a viseme recognizer (“lip reading system”) based on an active appearance model and by optimizing the recognition system to improve the translation output. The system outperforms the standard approach of separate recognition and translation.