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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)
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)
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)
The performance of telephone-based speaker verification systems can be severely degraded by the acoustic mismatch caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the mismatch. Specifically, a GMM-based handset selector is trained to identify the most likely handset used by the(More)
—Many frustrating experiences have been encountered when the training of neural networks by local search methods becomes stagnant at local optima. This calls for the development of more satisfactory search methods such as evolutionary search. However, training by evolutionary search can require a long computation time. In certain situations, using(More)
Since 2008, interview-style speech has become an important part of the NIST Speaker Recognition Evaluations (SREs). Unlike telephone speech, interview speech has lower signal-to-noise ratio, which necessitates robust voice activity detectors (VADs). This paper highlights the characteristics of interview speech files in NIST SREs and discusses the(More)
The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This paper introduces a new method---PairProSVM---to automatically predict the subcellular locations of proteins. The profiles of all protein sequences in the training set are constructed(More)