This paper describes a source modeling method for hidden Markov model (HMM) based speech synthesis for improved naturalness. A speech corpus is rst decomposed into the glottal sou...
Tuomo Raitio, Antti Suni, Hannu Pulakka, Martti Va...
The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for char...
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amara...
The problem of automatic recognition of human activities is among the most important and challenging open areas of research in Computer Vision. This paper presents a new approach ...
Arcangelo Distante, I. Gnoni, Marco Leo, Paolo Spa...
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal ...