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Abstract

In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical co-occurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.

Available Versions of this Item

• Educational video classification by using a transcript to image transform and supervised learning. (deposited 30 Nov 2017 14:15)
  • Educational video classification by using a transcript to image transform and supervised learning. (deposited 10 Jan 2018 12:52)
    • Educational video classification by using a transcript to image transform and supervised learning. (deposited 10 Jan 2018 15:51) [Currently Displayed]