This paper presents a technique for estimating the degree or intensity of emotional expressions and speaking styles appeared in speech. The key idea is based on a style control technique for speech synthesis using multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse process of the style control. We derive an algorithm for estimating pred...

The conformational kinetics of enzymes can be reliably revealed when they are governed by Markovian dynamics. Hidden Markov Models (HMMs) are appropriate especially in the case of conformational states that are hardly distinguishable. However, the evolution of the conformational states of proteins mostly shows non-Markovian behavior, recognizable by non-monoexponential state dwell time histogra...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit...

In HMM-based speech synthesis, it is important to correctly model duration because it has a significant effect on the perceptual quality of speech, such as rhythm. For this reason, hidden semi-Markov model (HSMM) is commonly used to explicitly model duration instead of using the implicit state duration model of HMM through its transition probabilities. The cost of using HSMM to improve duration...

Analysis and interpretation of the human electrocardiogram (ECG) is seriously impaired by high levels of noise and a wide variety of signal morphologies. These problems relate to the cardiac state of the patient and nature of the sensing process. A useful property, specific to the ECG signal in contrast to for instance EEG signals, is its evident cyclical nature. A typical ECG follows a fixed s...

We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). There are five states describing the trend in first level. Second and third levels are abstract and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised lea...

ity or acoustic observations conditioned on the state in Tied-mixture (or semi-continuous) distributions are an imhidden-Markov models (11MM), or for the case of the portant tool for acoustic modeling, used in many highSSM, conditioned on a region of the model. Some of the performance speech recognition systems today. This paper options that have been investigated include discrete dispiovides a...

This paper presents a new method for fuzzy system reliability analysis based on fuzzy semi-markov model with fuzzy transitions. The definition and the basic equation for interval transitions of a semi-markov model with fuzzy transitions are provided. The transitions for the above model with fuzzy states is provided for the non-homogeneous case. We apply this fuzzy technique to ATM transactions ...

For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of labeled training data, or the limited recognition cabability caused by very domain-specific modeling. This paper puts forward a semi-supervised incremental strategy for the robot to learn the manipulative tasks performed by...