Speaker-Independent Speech Recognition using Visual Features

Abstract

Visual Speech Recognition aims at transcribing lip movements into readable text. There have been many strides in automatic speech recognition systems that can recognize words with audio and visual speech features, even under noisy conditions. This paper focuses only on the visual features, while a robust system uses visual features to support acoustic features. We propose the concatenation of visemes (lip movements) for text classification rather than a classic individual viseme mapping. The result shows that this approach achieves a significant improvement over the state-of-the-art models. The system has two modules; the first one extracts lip features from the input video, while the next is a neural network system trained to process the viseme sequence and classify it as text.