Traditional statistical speech recognition systems typically make strong assumptions about the independence of observation frames and generally do not make use of segmental information. In contrast, when the segmentation is known, existing classifiers can readily accommodate segmental information in the decision process. We describe an approach to connected word recognition that allows the use ...

The session focused on acoustic modeling for speech recognition ; which can be segmented into three broad sub-areas: (1) feature extraction, (2) modeling the features for the speech source, and (3) estimation of the model parameters. The papers in this session touched on all of these areas. Huang focuses on the feature representation. Furui et al., Austin et al., and Kimbal et al. discuss new m...

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...