Experimenting with lipreading for large vocabulary continuous speech recognition

Vast majority of current research in the area of audiovisual speech recognition via lipreading from frontal face videos focuses on simple cases such as isolated phrase recognition or structured speech, where the vocabulary is limited to several tens of units. In this paper, we diverge from these traditional applications and investigate the effect of incorporating the visual and also depth information in the task of continuous speech recognition with vocabulary size ranging from several hundred to half a million words. To this end, we evaluate various visual speech parametrizations, both existing and novel, that are designed to capture different kind of information in the video and depth signals. The experiments are conducted on a moderate sized dataset of 54 speakers, each uttering 100 sentences in Czech language. Both the video and depth data was captured by the Microsoft Kinect device. We show that even for large vocabularies the visual signal contains enough information to improve the word accuracy up to 22% relatively to the acoustic-only recognition. Somewhat surprisingly, a relative improvement of up to 16% has also been reached using the interpolated depth data.