In this paper, we first introduce the use of Gaussian mixture models (GMM) for Chinese tone classification in continuous speech. Then, we explain how to integrate it with the HMM-based speech recognition system. Finally, we provide the tone classification accuracy of this probabilistic method which is tested with Chinese continuous speech database of national “863” project.

Gaussian mixture models are a very successful method for modeling the output distribution of a state in a hidden Markov model (HMM). However, this approach is limited by the assumption that the dynamics of speech features are linear and can be modeled with static features and their derivatives. In this paper, a nonlinear mixture autoregressive model is used to model state output distributions (...

Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust scene-level behaviour interpretation. In particular, we develop a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) to interpret group activities involving multiple objects captured in an outdoor scene. The model...

We present a method for learning characteristic motion patterns of mobile agents. The method works on two levels. On the first level, it uses the expectation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents’ movement. On the second level, agents’ trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently...

This paper examines audio-visual speaker veri cation using a novel adaptation of fused hidden Markov models, in comparison to output fusion of individual classi ers in the audio and video modalities. A comparison of both hidden Markov model (HMM) and Gaussian mixture model (GMM) classi ers in both modalities under output fusion shows that the choice of audio classi er is more important than vid...

We present a model for discourse coherence which combines the local entitybased approach of (Barzilay and Lapata, 2005) and the HMM-based content model of (Barzilay and Lee, 2004). Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt (Barz...

This thesis investigates how scene activity, which is observed by fixed surveillance cameras, can be modelled and learnt. Modelling of activity is performed through a spatio-probabilistic scene model that contains semantics like entry/exit zones, paths, junctions, routes and stop zones. The spatial nature of the model allows physical and semantic representation of the scene features, which can ...

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant to monitor and accurately analyze the gait of the patient. since walking shows the reproducibility signs in several phases,separating these phases can be used for the gait analysis. in this study, a method based on image processing for extracting phases of...

In this demo, we present a technique for synthesizing the mouth movement from acoustic speech information. The algorithm maps the audio parameter set to the visual parameter set using the Gaussian Mixture Model and the Hidden Markov Model. With this technique, we can create smooth and realistic lip movements.

Figure 1.1: Visualization of Tensors of different orders. gained popularity in parameter estimation for a variety of problems. In this lecture, the focus is on how they may be used in estimating the parameters of Gaussian Mixture Models and Hidden Markov Models. In a Gaussian mixture model, there are k unknown n-dimensional multivariate Gaussian distributions. Samples are generated by first pic...