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...

Automatic recognition of cursive handwritten script remains a challenging problem even with the promising improvement in classifier and computational power. Segmentation based approach for recognition of handwritten Urdu script has considerable computational overhead and has lower accuracy as compared to Roman and Chinese script due to additional segmentation error. Presence of complimentary ch...

Ž . Models of human control strategy HCS , which accurately emulate dynamic human behavior, have far reaching potential in areas ranging from robotics to virtual reality to the intelligent vehicle highway project. A number of learning algorithms, including fuzzy logic, neural networks, and locally weighted regression exist for modeling continuous human control strategies. These algorithms, howe...

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...