A new hidden Markov model (HMM) for the space-time evolution of daily rainfall is developed which models precipitation within hidden regional weather types by censored power-transformed Gaussian distributions. The latter provide flexible and interpretable multivariate models for the mixed discrete-continuous variables that describe both precipitation, when it occurs, and no precipitation. The m...

This paper describes a technique for modeling and controlling emotional expressivity of speech in HMM-based speech synthesis. A problem of conventional emotional speech synthesis based on HMM is that the intensity of an emotional expression appearing in synthetic speech completely depends on the database used for model training. To take into account the emotional expressivity that listeners act...

Behavioral models of human operators engaged in complex, time-critical high-risk domains, such as those typical in Human Supervisory Control (HSC) settings, are of great value because of the high cost of operator failure. We propose that Hidden Semi-Markov Models (HSMMs) can be employed to model behaviors of operators in HSC settings where there is some intermittent human interaction with a sys...

This paper describes spontaneous dialogue speech synthesis based on multiple-regression hidden semi-Markov model (MRHSMM), which enables users to specify paralinguistic information of synthesized speech with a dimensional representation. Paralinguistic aspects of synthesized speech are controlled by multiple regression models whose explanatory variables are abstract dimensions such as pleasant-...

Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and u...

This paper presents a statistical method that estimates a sequence of musical notes from a vocal F0 trajectory. Since the onset times and F0s of sung notes are considerably deviated from the discrete tatums and pitches indicated in a musical score, a score model is crucial for improving timefrequency quantization of the F0s. We thus propose a hierarchical hidden semi-Markov model (HHSMM) that c...

This paper presents a method by which a reinforcement learning agent can solve the incomplete perception problem using memory. The agent uses a hidden Markov model (HMM) to represent its internal state space and creates memory capacity by splitting states of the HMM. The key idea is a test to determine when and how a state should be split: the agent only splits a state when doing so will help t...

In this paper, we model a stock dynamic using Hidden Markov Model (HMM) where weekly return is normally distributed with mean and variance depending on the hidden random variable representing the stock trend. Using Expectation-Maximization algorithm, we estimate all the parameters and design a simple trading strategy based on it. We then compare its performance with Buy and Hold and Resistance ...