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Even though the subject of speaker verification has been investigated for several decades, numerous challenges and new opportunities in robust recognition techniques are still being explored. In this overview paper we first provide a brief introduction to statistical pattern recognition techniques that are commonly used for speaker verification. The second(More)
In this paper, we present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as “Sparse Auditory Reproducing Kernel” (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal(More)
This paper introduces a fully automatic chromosome classification algorithm for Multicolor or Multiplex Fluorescence In-Situ Hybridization (M-FISH) images using Gaussian mixture model technique. M-FISH is a recently developed cellular imaging method for rapid detection of chromosomal abnormalities, where each chromosome is labeled with 5 dyes and(More)
Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. With sub-wavelength distances between the microphones, resolving acute localization cues become difficult due to precision artifacts. In this paper we propose a framework which overcomes(More)
Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficient to(More)
In this paper, we present a non-linear filtering approach for extracting noise-robust speech features that can be used in a speaker verification task. At the core of the proposed approach is a time-series regression using Reproducing Kernel Hilbert Space (RKHS) based methods that extracts discriminatory non-linear signatures while filtering out the(More)
Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density microphone arrays where distance between sensor elements is much smaller than the wavelength of the signal of interest. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is(More)
In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN(More)