This paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a Hierarchical Hidden Markov Model (HHMM). This model makes use...
In this paper, we propose a new gesture recognition model for a set of both one-hand and two-hand gestures based on the dynamic Bayesian network framework which makes it easy to r...
Abstract. Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult ...
Daniel Meyer-Delius, Christian Plagemann, Georg vo...
—We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs) and audio models. SIFT features were extracted from all the im...
It is generally known that a well-designed excitation produces high quality signals in hidden Markov model (HMM)-based speech synthesis systems. This paper proposes a novel techni...
June Sig Sung, Doo Hwa Hong, Kyung Hwan Oh, Nam So...