1. A computer-implemented method for training a speech recognition computer system, comprising: under the control of one or more computer systems configured with executable instructions, receiving video data of a user speaking a sequence of words, the video data including first time stamp information, wherein the first time stamp information corresponds to a video recordation time indicative of a live time of the user speaking; receiving audio data of the user speaking the sequence of words, the audio data including second time stamp information, wherein the second time stamp information corresponds to an audio recordation time indicative of the live time of the user speaking; time synchronizing the video data and audio data based on the first time stamp information and the second time stamp information; segmenting the video data based, at least in part, on a first facial feature and a second facial feature of the user to determine facial segmentation data, wherein determining the facial segmentation data includes: a) identifying, from the video data, a first video frame that includes visual representations of an eye and lips of the user; b) determining, a first location of the eye in the first video frame and a second location of the lips in the first video frame; c) performing edge detection on the first and second locations to identify borders of the eye and the lips; d) generating a discrete set of sample bits corresponding to the first location of the eye and the second location of the lips in the first video frame; and e) creating a digital representation of the first facial feature and the second facial feature using the discrete set of sample bits segmenting the audio data, the segmenting including: a) parsing the audio data into word-segmented audio data; and b) parsing the word-segmented audio data into a phoneme-segmented audio data; overlaying the facial segmentation data, at a first time stamp of the video data, and the phoneme-segmented audio data, at a first time stamp of the audio data based at least in part on the time synchronizing; and updating a user-specific phoneme model for use in a speech recognition system, the phoneme model employing the overlaid facial segmentation data and phoneme-segmented audio data according to a machine-learning algorithm.