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The performance of automated lip reading using visemes as a classification schema has achieved less success compared with the use of ASCII characters and words largely due to the problem of different words sharing identical visemes. The Generative Pre-Training transformer is an effective autoregressive language model used for many tasks in Natural Language Processing, including sentence prediction and text classification. This paper proposes a new application for this model and applies it in the context of lip reading, where it serves as a language model to convert visual speech in the form of visemes, to language in the form of words and sentences. The network uses the search for optimal perplexity to perform the viseme-to-word mapping and is thus a solution to the one-to-many mapping problem that exists whereby various words that sound different when spoken look identical. This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known. Sentences from the benchmark BBC dataset "Lip Reading Sentences in the Wild"(LRS2), are classified with a character error rate of 10.7% and a word error rate of 18.0%. The main contribution of this paper is to propose a method of predicting words through the use of perplexity analysis when only visual cues are present, using an autoregressive language model.