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Building synthetic child voices is considered a difficult task due to the challenges associated with data collection. As a result, speaker adaptation in conjunction with Hidden Markov Model (HMM)-based synthesis has become prevalent in this domain because the approach caters for limited amounts of data. An initial average voice model is trained using data from multiple speakers and adapted to resemble...
In this paper, we investigate methods to improve the recognition performance of low-resource languages with limited training data by borrowing subspace parameters from a high-resource language in subspace Gaussian mixture model (SGMM) framework. As a first step, only the state-specific vectors are updated using low-resource language, while retaining all the globally shared parameters from the high-resource...
This paper presents a new similarity measure and matching scheme for content-based image retrieval (CBIR), based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. The paper proposes to calculate first a universal image model (UIM), which is built based on a large set of images. The derived UIM is then used as a reference for the calculation of adapted...
In gait-based person identification, statistical methods such as hidden Markov models (HMMs) have been proved to be effective. Their performance often degrades, however, when the amount of training data for each walker is insufficient. In this paper, we propose walker adaptation and walker adaptive training, where the data from the other walkers are effectively utilized in the model training. In walker...
Online handwriting recognition of Arabic script is a difficult problem since it is naturally both cursive and unconstrained. The analysis of Arabic script is further complicated due to obligatory dots/stokes that are placed above or below most letters and usually are written delayed in order. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties...
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