Computing phonological generalization over real speech exemplars

Though it has attracted growing attention from phonologists and phoneticians Exemplar Theory (e g Bybee 2001) has hitherto lacked an explicit production model that can apply to speech signals An adequate model must be able to generalize but this presents the problem of how to generate an output that generalizes over a collection of unique variable-length signals Rather than resorting to a priori phonological units such as phones we adopt a dynamic programming approach using an optimization criterion that is sensitive to the frequency of similar subsequences within other exemplars the Phonological Exemplar-Based Learning System We show that PEBLS displays pattern-entrenchment behaviour central to Exemplar Theory s account of phonologization (C) 2010 Elsevier Ltd All rights reserve

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm