CONF kurimo-esca99b/IDIAP Latent Semantic Indexing by Self-Organizing Map Kurimo, Mikko Mokbel, Chafic EXTERNAL http://publications.idiap.ch/attachments/papers/1999/kurimo99.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/kurimo-esca99 Related documents ESCA ETRW workshop on Accessing Information in Spoken Audio 1999 Cambridge, UK April 1999 25-30 IDIAP-RR 99-12 An important problem for the information retrieval from spoken documents is how to extract those relevant documents which are poorly decoded by the speech recognizer. In this paper we propose a stochastic index for the documents based on the Latent Semantic Analysis (LSA) of the decoded document contents. The original LSA approach uses Singular Value Decomposition to reduce the dimensionality of the documents. As an alternative, we propose a computationally more feasible solution using Random Mapping (RM) and Self-Organizing Maps (SOM). The motivation for clustering the documents by SOM is to reduce the effect of recognition errors and to extract new characteristic index terms. Experimental indexing results are presented using relevance judgments for the retrieval results of test queries and using a document perplexity defined in this paper to measure the power of the index models. REPORT kurimo-esca99/IDIAP Latent Semantic Indexing by Self-Organizing Map Kurimo, Mikko Mokbel, Chafic EXTERNAL http://publications.idiap.ch/attachments/reports/1999/rr99-12.pdf PUBLIC Idiap-RR-12-1999 1999 IDIAP Published in Proceedings of the ESCA ETRW workshop on Accessing Information in Spoken Audio, Cambridge, UK, 1999 An important problem for the information retrieval from spoken documents is how to extract those relevant documents which are poorly decoded by the speech recognizer. In this paper we propose a stochastic index for the documents based on the Latent Semantic Analysis (LSA) of the decoded document contents. The original LSA approach uses Singular Value Decomposition to reduce the dimensionality of the documents. As an alternative, we propose a computationally more feasible solution using Random Mapping (RM) and Self-Organizing Maps (SOM). The motivation for clustering the documents by SOM is to reduce the effect of recognition errors and to extract new characteristic index terms. Experimental indexing results are presented using relevance judgments for the retrieval results of test queries and using a document perplexity defined in this paper to measure the power of the index models.