By Damien Douxchamps, Nick Campbell (auth.), Andrei Popescu-Belis, Steve Renals, Hervé Bourlard (eds.)

ISBN-10: 3540781544

ISBN-13: 9783540781547

ISBN-10: 3540781552

ISBN-13: 9783540781554

This e-book constitutes the completely refereed post-proceedings of the 4th foreign Workshop on laptop studying for Multimodal interplay, MLMI 2007, held in Brno, Czech Republic, in June 2007.

The 25 revised complete papers offered including 1 invited paper have been rigorously chosen in the course of rounds of reviewing and revision from 60 workshop displays. The papers are geared up in topical sections on multimodal processing, HCI, person experiences and functions, picture and video processing, discourse and discussion processing, speech and audio processing, in addition to the PASCAL speech separation challenge.

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Extra info for Machine Learning for Multimodal Interaction: 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers

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7%. Chance performance on this task is around 1%. Note that it is possible to achieve better performance (58%) by using 30 training images per category [8], however, that would not leave enough test images for some of the categories. 5%. e. 2%. 44 K. Saenko and T. Darrell 100% image only speech only product rule mean rule 90% accuracy 80% 70% 60% 50% 40% clean 10db 4db 0db SNR of speech noise −4db Fig. 3. Absolute improvement across noise conditions on the test set. The Y-axis shows the percent of the test samples classified correctly, the X-axis shows the SNR of the noise condition.

HoloMAS 2007. LNCS (LNAI), vol. 4659, pp. 104–113. Springer, Heidelberg (2007) 12. : Data mining. Practical machine learning tools and techniques, 2nd edn. edu Abstract. Multimodal scene understanding is an integral part of humanrobot interaction (HRI) in situated environments. , any chair vs. ) Humans use multiple modalities to understand which object category is being referred to, simultaneously interpreting gesture, speech and visual appearance, and using one modality to disambiguate the information contained in the others.

Even if the visual classifier is not accurate enough to choose the correct category from the set of all possible categories, it may be good enough to choose between a few acoustically similar categories. The same intuition applies in the other direction, with speech disambiguating confusable visual categories. For example, Figure 1 shows the categories that the visual classifier confused the most in our experiments. g. [12], [10]). Although visual object category recognition is a well-studied problem, to the best of our knowledge, it has not been combined with speech-based category recognition.

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Machine Learning for Multimodal Interaction: 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers by Damien Douxchamps, Nick Campbell (auth.), Andrei Popescu-Belis, Steve Renals, Hervé Bourlard (eds.)


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