Abstract
Assessing the quality of a speaker localization or tracking algorithm on a few short examples is difficult, especially when the ground-truth is absent or not well defined. One step towards systematic performance evaluation of such algorithms is to provide time-continuous speaker location annotation over a series of real recordings, covering various test cases. Areas of interest include audio, video and audio-visual speaker localization and tracking. The desired location annotation can be either 2-dimensional (image plane) or 3-dimensional (physical space). This paper motivates and describes a corpus of audio-visual data called “AV16.3”, along with a method for 3-D location annotation based on calibrated cameras. “16.3” stands for 16 microphones and 3 cameras, recorded in a fully synchronized manner, in a meeting room. Part of this corpus has already been successfully used to report research results.
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Lathoud, G., Odobez, JM., Gatica-Perez, D. (2005). AV16.3: An Audio-Visual Corpus for Speaker Localization and Tracking. In: Bengio, S., Bourlard, H. (eds) Machine Learning for Multimodal Interaction. MLMI 2004. Lecture Notes in Computer Science, vol 3361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30568-2_16
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DOI: https://doi.org/10.1007/978-3-540-30568-2_16
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