Abstract
Current storage and processing facilities have caused the emergence of many multimedia repositories and, consequently, they have also triggered the necessity of new approaches for information retrieval. In particular, spoken document retrieval is a very complex task since existing speech recognition systems tend to generate several transcription errors (such as word substitutions, insertions and deletions). In order to deal with these errors, this paper proposes an enriched document representation based on a phonetic codification of the automatic transcriptions. This representation aims to reduce the impact of the transcription errors by representing words with similar pronunciations through the same phonetic code. Experimental results on the CL-SR corpus from the CLEF 2007 (which includes 33 test topics and 8,104 English interviews) are encouraging; our method achieved a mean average precision of 0.0795, outperforming all except one of the evaluated systems at this forum.
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Reyes-Barragán, M.A., Villaseñor-Pineda, L., Montes-y-Gómez, M. (2008). A Soundex-Based Approach for Spoken Document Retrieval. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_19
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DOI: https://doi.org/10.1007/978-3-540-88636-5_19
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