Multi-modal Language Models for Lecture Video Retrieval

We propose Multi-modal Language Models (MLMs), which adapt latent variable techniques for document analysis to exploring co-occurrence relationships in multi-modal data. In this paper, we focus on the application of MLMs to indexing text from slides and speech in lecture videos, and subsequently employ a multi-modal probabilistic ranking function for lecture video retrieval. The MLM achieves highly competitive results against well established retrieval methods such as the Vector Space Model and Probabilistic Latent Semantic Analysis. When noise is present in the data, retrieval performance with MLMs is shown to improve with the quality of the spoken text extracted from the video.