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This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification(More)
Prediction of protein subcellular localization is an important yet challenging problem. Recently, several computational methods based on Gene Ontology (GO) have been proposed to tackle this problem and have demonstrated superiority over methods based on other features. Existing GO-based methods, however, do not fully use the GO information. This paper(More)
This paper reviews diierent approaches to improving the real time recurrent learning (RTRL) algorithm and attempts to group them into common frameworks. The characteristics of sub-grouping strategy, mode exchange RTRL, and cellular genetic algorithms are discussed. The relationships between these algorithms are highlighted and their time complexities and(More)
One key factor that hinders the widespread deployment of speaker verification technologies is the requirement of long enrollment utterances to guarantee low error rate during verification. To gain user acceptance of speaker verification technologies, adaptation algorithms that can enroll speakers with short utterances are highly essential. To this end, this(More)
Recent research has demonstrated the merit of combining Gaussian mixture models and support-vector-machine (SVM) for text-independent speaker verification. However , one unaddressed issue in this GMM–SVM approach is the imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM.(More)
The success of the recent i-vector approach to speaker verification relies on the capability of i-vectors to capture speaker characteristics and the subsequent channel compensation methods to suppress channel variability. Typically, given an utterance, an i-vector is determined from the utterance regardless of its length. This paper investigates how the(More)
We apply the ETSI's DSR standard to speaker verification over telephone networks and investigate the effect of extracting spectral features from different stages of the ETSI's front-end on speaker verification performance. We also evaluate two approaches to creating speaker models, namely maximum likelihood (ML) and maximum a posteriori (MAP), in the(More)
This paper presents an approach that uses articulatory features (AF) derived from spectral features for telephone-based speaker verification. To minimize the acoustic mismatch caused by different handsets, handset-specific normalization is applied to the spectral features before the AF are extracted. Experimental results based on 150 speakers using 10(More)
The real-time recurrent learning (RTRL) algorithm, which is originally proposed for training recurrent neural networks, requires a large number of iter ations for convergence because a small learning rate should be used. While an obvious solution to this problem is to use a large learning rate, this could result in undesirable convergence characteristics.(More)
This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to temporal pattern classifications and predictions. Based on the architecture of the conventional Radial Basis Function networks, the RRBFNs use Gaussian nonlinearity and have feedback paths between every hidden node. These feedback paths enable the networks to learn(More)