This work presents a new RGB-D acquisition system to capture a comprehensive dynamic facial dataset that can be used for visual speech recognition. The RGB-D facial dataset acquisition system uses a Kinect to record detailed facial features of a person. The dynamic facial dataset is comprised of the facial data of 20 individuals saying 20 common English words or phrases. The acquisition system employs Kinect facial tracking, which records a large number of dynamic facial features. These features include: facial points, facial outline, RGB data, depth data, mapping between RGB and depth data, facial animation units, facial shape units, and finally 2D and 3D face representations of the face along with the 3D head orientation. The effectiveness of acquired RGB-D dynamic facial dataset is demonstrated by presenting a new visual speech recognition method that employs three-dimensional spatiotemporal data of different facial feature points. A number of visual speech recognition methods from the literature are also tested on the new dataset and they obtain a comparable or favorable visual speech recognition results. The results demonstrate the effectiveness of the proposed RGB-D dynamic facial dataset and show that it can be effectively employed in a visual speech recognition system.