Semantic context inference for spoken document retrieval using term association matrices

This study presents a novel approach to semantic context inference based on term association matrices for spoken document retrieval. Each recognized term in a spoken document infers a semantic vector containing a bag of semantic terms from a term association matrix. Such a semantic term expansion and re-weighting make the semantic context inference vector a suitable representation for speech indexing. We consider both words and syllables on term association matrices for semantic context inference. The syllable lattice bigram instead of the single-best speech recognition results and various term weighting schemes have been studied for semantic context inference. Experiments were conducted on Mandarin Chinese broadcast news. The results indicate the proposed approach offers a significant performance improvement of spoken document retrieval.