We present a novel method to correct automatically generated speech transcripts of talks and lecture videos using text from accompanying presentation slides. The approach finesses the challenges of dealing with technical terms which are often outside the vocabulary of speech recognizers. Further, we align the transcript to the slide word sequence so that we can improve the organization of closed captioning for hearing impaired users, and improve automatic highlighting or magnification for visually impaired users. For each speech segment associated with a slide, we construct a sequential Hidden Markov Model for the observed phonemes that follows slide word order, interspersed with text not on the slide. Incongruence between slide words and mistaken transcript words is accounted for using phoneme confusion probabilities. Hence, transcript words different from aligned high probability slide words can be corrected. Experiments on six talks show improvement in transcript accuracy and alignment with slide words.