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Spoken document retrieval by discriminative modeling in a high dimensional feature space

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4 Author(s)
Oba, T. ; NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan ; Hori, T. ; Nakamura, A. ; Ito, A.

This paper proposes discriminative modeling in a high dimensional feature space for spoken document retrieval (SDR). To estimate the parameters of a high dimensional model properly, a large quantity of data is necessary, but there is no such large corpus for document retrieval. This paper employs two approaches to overcome this problem. One is a reranking approach. A baseline system first gives each document a score and then the score is compensated by employing a high dimensional model. The other approach is automatic query generation. A large number of queries are automatically generated and used for parameter estimation. Our experimental result shows that our proposed method can greatly improve SDR performance.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012