Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...
In this paper, we investigate the use of the coupled hidden Markov models (CHMM) for the task of audio-visual text dependent speaker identification. Our system determines the iden...
Tieyan Fu, Xiao Xing Liu, Lu Hong Liang, Xiaobo Pi...
The advent of the smartphone as a highly complex technology has been accompanied by mobile operating systems (OS), large communities of developers, diverse content providers, and ...
We address several challenges for applying statistical dialog managers based on Partially Observable Markov Models to real world problems: to deal with large numbers of concepts, ...
Sebastian Varges, Giuseppe Riccardi, Silvia Quarte...
In this paper we detail the synergies we have observed between the features and limitations of mobile phones, and the usability and accessibility requirements of rural developing ...