Semantic query expansion and context-based discriminative term modeling for spoken document retrieval
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Pages
5085-5088
Date Issued
2012
Author(s)
Abstract
In this paper, we propose a semantic query expansion approach by extending the query-regularized mixture model to include latent topics and apply it to spoken documents. We also propose to use context feature vectors for spoken segments to train SVM models to enhance the posterior-weighted normalized term frequencies in lattices. Experiments on Mandarin broadcast news showed that this approach offered good improvements when applied on spoken documents including relatively high recognition errors.
SDGs
Type
conference paper