Hidden Markov Models have been used frequently in the audio domain to identify underlying musical structure. Much less work has been done in the purely symbolic realm. Recently, a substantial amount of expertlabelled symbolic musical data has been injected into the research community. The new availability of data allows for the application of machine learning models to purely symbolic tasks. Si...

Hidden Markov Model theory is an extension of the Markov Model process. It has found uses in such areas as speech recognition, target tracking and word recognition. One area which has received little in the way of research interest, is the use of Hidden Markov Models in character recognition. In this paper the application of Hidden Markov Model theory to dynamic character recognition is investi...

Meaningful objects in a scene move with purpose. In this work, a Hidden Markov Model is used to represent the “hidden” regularities behind the apparent object movement in a scene and reproduce such regularities as “blind” expectations. By adapting the weights on beliefs with new visual evidence, an Augmented Hidden Markov Model is used to produce dynamic expectations of moving objects in the sc...

This paper extends the scope of the model of regulation of social exchanges based on the concept of a supervisor of social equilibrium. We allow the supervisor to interact with personality-based agents that control the supervisor access to their internal states, behaving either as transparent agents (agents that allow full external access to their internal states) or as non-transparent agents (...

This paper describes Hidden Markov Model and its application in natural language process, first introduces the basic concept of Hidden Markov Model, then introduces the three basic issues and the basic algorithm to solve the problems, finally gives the demonstration of the application of Chinese part-of-speech tagging and speech recognition via Hidden Markov Model.