Learning spoken document similarity and recommendation using supervised probabilistic latent semantic analysis
K. Thambiratnam, Frank Torsten Bernd Seide · 2007
This paper presents a model-based approach to spoken docu-ment similarity called Supervised Probabilistic Latent Seman-tic Analysis (PLSA). The method differs from traditional spo-ken document similarity techniques in that it allows similarity to be learned rather than approximated. The ability to learn similarity is desirable in applications such as Internet video recommendation, in which complex relationships like user-preference or speaking style need to be predicted. The pro-posed method exploits prior knowledge of document relation-ships to learn similarity. Experiments on broadcast news and Internet video corpora yielded 16.2 % and 9.7 % absolute mAP gains over traditional PLSA. Additionally, a cascaded Super-vised+Discriminative PLSA system achieved a 3.0 % absolute mAP gain over a Discriminative PLSA system, demonstrat-ing the complementary nature of Supervised and Discriminative