Download Adaptive Multimedia Retrieval. Large-Scale Multimedia by Hisham Mohamed, Marc von Wyl, Eric Bruno, Stéphane PDF

By Hisham Mohamed, Marc von Wyl, Eric Bruno, Stéphane Marchand-Maillet (auth.), Marcin Detyniecki, Ana García-Serrano, Andreas Nürnberger, Sebastian Stober (eds.)

ISBN-10: 3642374247

ISBN-13: 9783642374241

This publication constitutes the refereed post-proceedings of the ninth foreign convention on Adaptive Multimedia Retrieval, AMR 2011, held in Barcelona, Spain, in July 2011. The nine revised complete papers and the invited contribution provided have been conscientiously reviewed and chosen from various submissions. The papers conceal themes starting from theoretical paintings to functional implementations and its evaluate, such a lot of them facing audio or track media. they're equipped in topical sections on assessment and person reports, audio and track, photo retrieval, and similarity and music.

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Extra info for Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation: 9th International Workshop, AMR 2011, Barcelona, Spain, July 18-19, 2011, Revised Selected Papers

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62 % 2% 2% Weather Forecasts (in Spanish) 13 10 min aprox. RTVE 1 * 5% 0% *All segments with background music The three last trainings were decomposed in two sub-scenarios due to DNS facility to train the user model using audio files and their corresponding transcription (notice that the DNS Spanish model has been used). So, we distinguish among: Short enrollment, where DNS was trained using one video with a length of, approximately, 10 minutes; and Long enrollment, where the DNS user model was trained using 7 videos with a mean length of 10 minutes).

Evaluation with specific vocabularies. The ASR system is customized to work on a limited vocabulary previously defined. Evaluation combining specific vocabulary and speaker dependence. In this scenario the ASR system will be trained and tested with videos of a specific subject of a specific speaker. Evaluation without language model. Language models define the linguistic rules of a language and this knowledge allows the ASR system refines the options during recognition discarding linguistically invalid options and selecting those more grammatically appropriate.

33–42, 2013. © Springer-Verlag Berlin Heidelberg 2013 34 M. González et al. an important issue arises, which of these products best suites information retrieval system needs? There have been great efforts in ASR evaluation frameworks, particularly with some conferences devoted to ASR evaluation but they are, in general, designed from the point of view of final applications such as those promoted by TC-STAR1 (Technology and Corpora for Speech to Speech Translation) focusing on Speech-to-Speech Translation or the Spoken Document Retrieval Task promoted by TREC2 (Text Retrieval Evaluation Conference) in late 90´s or CL-SDR (Cross Language Speech Document Retrieval) from 2003 to 2007 launched by CLEF3 (Cross Language European Forum).

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