A new scheme for the optimization of codebook sizes for HMMs and the generation of HMM ensembles is proposed in this paper. In a discrete HMM, the vector quantization procedure and the generated codebook are associated with performance degradation. By using a selected clustering validity index, we show that the optimization of HMM codebook size can be selected without training HMM classifiers. ...

Gesture and motion recognition are needed for a variety of applications. The use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. In particular, human-computer intelligent interaction has been a focus of research in vision-based gesture recognition. In this work, we introduce a 3-...

This article is concerned with an original approach to generative classification of spatiotemporal areal (or lattice) data based on implementation spatial weighting Hidden Markov Models (HMMs). In the framework this model at each unit specified by conditionally independent Gaussian observations and first-order chain for labels call it local HMM. The proposed modification conventional HMM spatia...

This paper presents an adaptive algorithm for compensating pronunciation variations in hidden Markov model (HMM) based speech recognition. The proposed method aims to adapt the HMM topology and the corresponding HMM parameters to meet the variations of speaker dialects. In adaptive HMM topology, two hypothesis test schemes are designed to detect whether a new speaking variation occurs in state/...

We present a formulation of the autoregressive HMM for speech synthesis and compare it to the standard HMM synthesis framework and the trajectory HMM. We give details of how to do efficient parameter estimation and synthesis with the autoregressive HMM and discuss consequences of the autoregressive HMM model. There are substantial similarities between the three models, which we explore. The adv...

We propose the autoregressive HMM for speech synthesis. We show that the autoregressive HMM supports efficient EM parameter estimation and that we can use established effective synthesis techniques such as synthesis considering global variance with minimal modification. The autoregressive HMM uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the stan...

Inflation is a major issue to be considered for the development of any nation as well as having great influence on worldwide economy. In this paper, we present a novel approach to analyze inflation data of a given time using HMM. HMM is being used for several computational problems in real life applications. Although, HMM is not a perfect way to predict future events. Here we use HMM, that is t...

Abstract Applying the gradient discretisation method (GDM), paper develops a comprehensive numerical analysis for nonlinear equations called reaction–diffusion model. Using only three properties, this provides convergence results several conforming and non-conforming schemes that align with GDM. As an application of analysis, hybrid mimetic mixed (HMM) model is designed, its established. Numeri...

The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than the first-order hidden Markov model (HMM). The relationship among DHO-HMM, high-order HMM (HO-HMM), hidden semi-Markov model (HSMM), and HMM is presented and discussed. We derived recursive forward and backward probability functions for the partial observation sequenc...