In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of nonparametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole hi...

In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowin...

The maximum-penalized-likelihood estimation for hidden Markov models with general observation densities is described. All statistical inference, including the model estimation, testing, and selection, is based on the restricted optimization of the penalized likelihood function with respect to the chosen model family. The method is used in an economic application, where stock market index return...

This paper presents a novel method for modeling phonetic context using linear context transforms. Initial investigations have shown the feasibility of synthesising context dependent models from context independent models through weighted interpolation of the peripheral states of a given hidden markov model with its adjacent model. This idea can be further extended, to maximum likelihood estimat...

Unsupervised segmentation of phoneme sequences is an essential process to obtain unknown words during spoken dialogues. In this segmentation, an input phoneme sequence without delimiters is converted into segmented sub-sequences corresponding to words. The Pitman-Yor semi-Markov model (PYSMM) is promising for this problem, but its performance degrades when it is applied to phonemelevel word seg...

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...

Image segmentation is discussed for years in numerous papers, but assessing its quality is mainly dealt with in recent works. Quality assessment is a primary concern for anyone working towards better segmentation tools. It both helps to objectively improve segmentation techniques and to compare performances with respect to other similar algorithms. In this paper we use a statistical framework t...

The paper presents our experiences with the phone transi tion acoustical models The phone transition models were compared to the traditional context dependent phone models We put special atten tion on the speech signal segmentation analysis to provide a better in sight into certain segmentation e ects when using the di erent acoustical models Experiments with the HMM based models were performed...

— The aim of the present paper is to document the need for adapting the definition of hidden Markov models (HMM) to population studies, which rigorous interpretation typically requires the use of mixed-effects models, as well as for corresponding learning methodologies. In this article, mixed hidden Markov models (MHMM) are introduced through a brief state of the art on hidden Markov models and...

Among the class of discrete time Markovian processes, two models are widely used, the Markov chain and the Hidden Markov Model. A major di erence between these two models lies in the relation between successive outputs of the observed variable. In a visible Markov chain, these are directly correlated while in hidden models they are not. However, in some situations it is possible to observe both...