In the present paper, hidden Markov model (HMM) based speech synthesis system developed in Nagoya Institute of Technology (Nitech-HTS) for a competition of text-to-speech synthesis systems using the same speech databases, named Blizzard Challenge 2005, is described. We show an overview of the basic HMM-based speech synthesis system and then recent developments to the latest one such as STRAIGHT...

We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once th...

We present an audio chord recognition system based on a generalization of the Hidden Markov Model (HMM) in which the duration of chords is explicitly considered a type of HMM referred to as a hidden semi-Markov model, or duration-explicit HMM (DHMM). We find that such a system recognizes chords at a level consistent with the state-of-the-art systems – 84.23% on Uspop dataset at the major/minor ...

Cognition is a complex and dynamic process. It is an essential goal to estimate latent attentional states based on behavioral measures in many sequences of behavioral tasks. Here, we propose a probabilistic modeling and inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed model extends the standard hidden Markov mode...

In this paper, a novel monitoring method for the repetitive batch operation with two-dimensional dynamic behavior is proposed. It combines dynamic multiway principal component analysis (DMPCA) and hidden segmental semi-Markov models (HSMM) to resolve the problem caused by the two-dimensional behavior of batch processes. DMPCA utilizes the batch-to-batch dynamic characteristics and eliminates th...

In this paper, we propose an improvement of hidden semiMarkov model (HSMM) based speech synthesis system by durationdependent state transition probabilities. In traditional HMM algorithm, the probability of the duration of a state decreases exponentially with time, which does not provide an adequate representation of the temporal structure of speech. To overcome this limitation, HSMM, which mod...

Multi-state Markov models have proven useful in many concrete situations and have had successful applications, notably in analysing disease history data. Continuous-time Markov models with three transient states are considered for the study of asthma control evolution in a cohort of 406 patients with persistent asthma. Firstly, the model is considered in the homogeneous case. Covariates are tak...

A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...