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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)
This paper presents an approach that uses articulatory features (AFs) 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 AFs are extracted. Experimental results based on 150 speakers using 10(More)
—The introduction of interview speech in recent NIST Speaker Recognition Evaluations (SREs) has necessitated the development of robust voice activity detectors (VADs) that can work under very low signal-to-noise ratio. This paper highlights the characteristics of interview speech files in NIST SREs and discusses the difficulties of detecting(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)