Topic identification and discovery on text and speech

Abstract

We compare the multinomial i-vector framework from the speech community with LDA, SAGE, and LSA as feature learners for topic ID on multinomial speech and text data. We also compare the learned representations in their ability to discover topics, quantified by distributional similarity to gold-standard topics and by human interpretability. We find that topic ID and topic discovery are competing objectives. We argue that LSA and i-vectors should be more widely considered by the text processing community as pre-processing steps for downstream tasks, and also speculate about speech processing tasks that could benefit from more interpretable representations like SAGE.