Learning Spoken Document Similarity and Recommendation using Supervised Probabilistic Latent Semantic Analysis

This paper presents a model-based approach to spoken document similarity called Supervised Probabilistic Latent Semantic Analysis (PLSA). The method differs from traditional spoken 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 proposed method exploits prior knowledge of document relationships 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 Supervised+Discriminative PLSA system achieved a 3.0% absolute mAP gain over a Discriminative PLSA system, demonstrating the complementary nature of Supervised and Discriminative PLSA training.