From audio to information: Learning topics from audio transcripts

Abstract

In this work, the technical feasibility of working with audio transcriptions from Youtube is analyzed, as well as presenting a method that allows data acquisition, pre-processing, and post-processing to work with this type of data. A topic modeling approach with the latent dirichlet allocation algorithm is used. An approach is also presented to dynamically determine the ideal number of topics that make up a given corpus. In the experiments, a database of 250 audio transcriptions was used, obtaining a model with coherence in the range of 40%.