Overall, the addition of the style and domain models reduces perplexity and WER by a noticeable 7.1% and 2.1%, respectively, as shown in Table 2  In addition to identifying content words, HMM-LDA also assigns words to a topic based on their distribution across documents.
we can apply HMM-LDA with 100 topics to the Textbook dataset to identify representative words and their associated contexts for each topic.