Conference Paper

Sparse Kernel Logistic Regression using Incremental Feature Selection for Text-Independent Speaker Identification

IESK, Magdeburg Univ.
DOI: 10.1109/ODYSSEY.2006.248115 Conference: Speaker and Language Recognition Workshop, 2006. IEEE Odyssey 2006: The
Source: IEEE Xplore

ABSTRACT Logistic regression is a well known classification method in the field of statistical learning. Recently, a kernelized version of logistic regression has become very popular, because it allows non-linear probabilistic classification and shows promising results on several benchmark problems. In this paper we show that kernel logistic regression (KLR) and especially its sparse extensions (SKLR) are useful alternatives to standard Gaussian mixture models (GMMs) and support vector machines (SVMs) in Speaker recognition. While the classification results of KLR and SKLR are similar to the results of SVMs, we show that SKLR produces highly sparse models. Unlike SVMs the kernel logistic regression also provides an estimate of the conditional probability of class membership. In speaker identification experiments the SKLR methods outperform the SVM and the GMM baseline system on the POLY-COST database

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    ABSTRACT: This study aims to explore the case of robust speaker recognition with multi-session enrollments and noise, with an emphasis on optimal organization and utilization of speaker information presented in the enrollment and development data. This study has two core objectives. First, we investigate more robust back-ends to address noisy multi-session enrollment data for speaker recognition. This task is achieved by proposing novel back-end algorithms. Second, we construct a highly discriminative speaker verification framework. This task is achieved through intrinsic and extrinsic back-end algorithm modification, resulting in complementary sub-systems. Evaluation of the proposed framework is performed on the NIST SRE2012 corpus. Results not only confirm individual sub-system advancements over an established baseline, the final grand fusion solution also represents a comprehensive overall advancement for the NIST SRE2012 core tasks. Compared with state-of-the-art SID systems on the NIST SRE2012, the novel parts of this study are: 1) exploring a more diverse set of solutions for low-dimensional i-Vector based modeling; and 2) diversifying the information configuration before modeling. All these two parts work together, resulting in very competitive performance with reasonable computational cost.
    Audio, Speech, and Language Processing, IEEE/ACM Transactions on. 08/2014;

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