AV Robot Command recognition @ ICMI & ICASSP

Xavier Alameda-Pineda, Jordi Sanchez and Radu Horaud

Keywords: audio-visual fusion, discriminative multimodal methods, robot command recognition.

We investigated the problem of choosing a classifier for audio-visual command recognition. Because such commands are culture- and user-dependant, methods need to learn new commands from a few examples. We benchmark three state-of-the-art discriminative classifiers based on bag of words and SVM. The comparison is made on monocular and monaural recordings of a publicly available dataset. We seek for the best trade off between speed, robustness and size of the training set. In the light of over 150,000 experiments, we conclude that this is a promising direction of work towards a flexible methodology that must be easily adaptable to a large variety of users.

The main publications are [1] and [2].


  1. J. Sanchez-Riera, X. Alameda-Pineda, and R. Horaud, “Audio-Visual Robot Command Recognition,” in IEEE/ACM International Conference on Multimodal Interaction, Santa Monica, USA, 2012, pp. 371-378. [ bib pdf ]
      author       = "Sanchez-Riera, Jordi and Alameda-Pineda, Xavier and Horaud, Radu",
      title        = "Audio-Visual Robot Command Recognition",
      booktitle    = "IEEE/ACM International Conference on Multimodal Interaction",
      year         = "2012",
      address = {Santa Monica, USA},
      pages = {371--378},
  2. X. Alameda-Pineda, J. Sanchez-Riera, and R. Horaud, “Benchmarking Methods for Audio-Visual Recognition Using Tiny Training Sets,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, 2013, pp. 3662-3666. [ bib pdf ]
      author       = "Alameda-Pineda, Xavier and Sanchez-Riera, Jordi and Horaud, Radu",
      title        = "Benchmarking  Methods for Audio-Visual Recognition Using Tiny Training Sets",
      booktitle    = "IEEE International Conference on Acoustics, Speech, and Signal Processing",
      year         = "2013",
      pages = {3662--3666},
      address = {Vancouver, Canada},

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